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This Robot Arm Can Feel Touch Like a Human · The Last Observer: Science and Beyond

• 0:00
  Speaker 1: Welcome to The Last Observer, a journey through science, history,
• 0:04
  Speaker 1: and the universe's greatest mysteries. From ancient enigmas to the
• 0:08
  Speaker 1: deepest questions at the frontiers of reality, we explore the discoveries, ideas,
• 0:13
  Speaker 1: and mysteries that challenge what we think we know. Stay curious.
• 0:18
  Speaker 1: Keep questioning. And let's search for the truth beyond the known.
• 0:27
  Speaker 2: Welcome to the Last Observer podcast. Today we will talk
• 0:31
  Speaker 2: about a robotic arm that can feel almost like a
• 0:33
  Speaker 2: human does across its entire body of the arm.
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  Speaker 3: Hold on, the whole arm? Not just the hand or
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  Speaker 3: the fingertips?
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  Speaker 2: Yes, the whole upper and lower arm. Researchers at the
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  Speaker 2: Hong Kong University of Science and Technology built something they
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  Speaker 2: call M-arm. It embeds soft, tactile skin right into the
• 0:52
  Speaker 2: robotic skeleton, and it can sense contact all over those surfaces.
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  Speaker 3: That is different from most robot arms I have heard about.
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  Speaker 3: Usually they are basically blind to touch, except maybe a
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  Speaker 3: force sensor at the wrist or something crude.
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  Speaker 1: Exactly.
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  Speaker 2: And this one not only feels, it can interpret what
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  Speaker 2: the touch means and decide how to move in response.
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  Speaker 2: They even showed it distinguishing five different kinds of human
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  Speaker 2: touch and responding with different expressions.
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  Speaker 3: Expressions? Like it looks happy or annoyed?
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  Speaker 2: Something like that. Happiness, sadness, curiosity, shyness, or anger, depending
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  Speaker 2: on whether you are stroking it, pinching it, poking it,
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  Speaker 2: tickling it, or hitting it. And it got those right
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  Speaker 2: almost every time.
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  Speaker 3: Okay, that is both impressive and a little unsettling. How
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  Speaker 3: accurate was it?
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  Speaker 2: Up to 99.8% in their tests. So basically, if you
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  Speaker 2: tickle this arm, it knows, and it reacts accordingly.
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  Speaker 3: I keep picturing someone absentmindedly patting a robot arm while
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  Speaker 3: it is working. and the arm suddenly looking shy. That
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  Speaker 3: would be a scene.
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  Speaker 2: It is a bit sci-fi, but the underlying idea is serious.
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  Speaker 2: Robots that only rely on cameras struggle in cluttered spaces
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  Speaker 2: or when something gets in the way of the view.
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  Speaker 2: Touch gives them another channel, and this system puts a
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  Speaker 2: lot of it on the arm itself.
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  Speaker 3: So the paper is in Nature Sensors, right? That is
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  Speaker 3: a solid journal. Yes.
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  Speaker 2: The team led by Professor Shen Yajing published the work there.
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  Speaker 2: The news about it came out just in the last
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  Speaker 2: day or so from the university and tech sites.
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  Speaker 3: Makes sense that it is getting attention now. Most robot
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  Speaker 3: arms are still pretty stiff about the world around them.
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  Speaker 3: Giving the arm itself a sense of touch feels like
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  Speaker 3: a real step towards something that can work safely near people.
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  Speaker 2: That is the direction they are pointing toward. Embodied intelligence,
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  Speaker 2: where the body and the sensing are part of how
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  Speaker 2: the robot understands and acts in the world, not just
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  Speaker 2: an afterthought.
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  Speaker 3: I want to understand how they actually built the sensing.
• 2:51
  Speaker 3: Is it like electronic skin with wires everywhere or something else?
• 2:56
  Speaker 2: It is optical. They put soft material on the outside
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  Speaker 2: and tiny cameras inside the arm that watch how the
• 3:02
  Speaker 2: skin deforms. That way they avoid a lot of wiring
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  Speaker 2: problems and it is more robust to interference.
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  Speaker 3: Cameras inside the arm looking at the skin. That is clever.
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  Speaker 3: So the arm is basically watching its own surface from within.
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  Speaker 2: Yes. And the resolution is high. They report it can
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  Speaker 2: distinguish two points of contact only 18 millimeters apart, which
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  Speaker 2: is better than typical human arm skin. Human fingertips are finer,
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  Speaker 2: but for an arm, that is impressive.
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  Speaker 3: Better than a human arm at telling close touches apart.
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  Speaker 3: That alone is surprising. Most robotic skins I have seen
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  Speaker 3: were coarser.
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  Speaker 2: And they covered more than 71,000 square millimeters of the
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  Speaker 2: upper and lower arms. That is a large area for
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  Speaker 2: continuous sensing.
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  Speaker 3: So not a few discrete sensors, a proper skin, Right.
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  Speaker 2: And importantly, adding all that soft sensing did not wreck
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  Speaker 2: the arm's precision or strength. It can still position accurately
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  Speaker 2: and take loads over 15 kilograms.
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  Speaker 3: That is the hard part, right? Soft and sensitive usually
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  Speaker 3: means fragile or imprecise. They managed both.
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  Speaker 2: It seems so. The design is inspired by bone, muscle,
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  Speaker 2: and skin working together. Rigid skeleton inside, soft sensing outside.
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  Speaker 2: and then a control loop that ties the feeling to
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  Speaker 2: the movement in real time.
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  Speaker 3: You said the cameras inside track the deformation of the skin.
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  Speaker 3: How do they turn that into something the robot can
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  Speaker 3: use quickly?
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  Speaker 2: They extract simple features from the tactile data in real time.
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  Speaker 2: Things like where the contact is, how large the area is,
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  Speaker 2: how hard the force is, and whether there is one
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  Speaker 2: contact or several. Then those features go straight into the
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  Speaker 2: motion controller.
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  Speaker 3: So they are not feeding raw high-dimensional images into some
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  Speaker 3: giant neural net every fraction of a second.
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  Speaker 2: No, and that was deliberate. The lead researcher pointed out
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  Speaker 2: that end-to-end learning on that kind of force field data
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  Speaker 2: would need huge amounts of labeled examples and a lot
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  Speaker 2: of compute, which is hard to run on the robot
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  Speaker 2: itself in real time.
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  Speaker 3: That makes practical sense. You want something that can react
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  Speaker 3: in 100 milliseconds or so without a data center.
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  Speaker 1: Yes.
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  Speaker 2: The system also mixes in the joint angles, so it
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  Speaker 2: knows its own pose and combines that with the touch information.
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  Speaker 2: Proprioception plus exteroception, as they put it.
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  Speaker 3: Like how we know where our arm is and also
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  Speaker 3: feel what is touching it.
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  Speaker 2: Exactly. And they tested that the sensing stays accurate even
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  Speaker 2: with different kinds of interference. Electromagnetic noise, light, temperature, humidity, sound.
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  Speaker 2: Fidelity stayed up around 97.9%.
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  Speaker 3: That is useful if you ever put this near factory
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  Speaker 3: equipment or outdoor conditions.
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  Speaker 2: And the spatial resolution is 0.085 millimeters, which they compare
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  Speaker 2: to human fingertip skin. For localization of contact, that is
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  Speaker 2: quite fine.
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  Speaker 3: I am still stuck on the two-point discrimination being better
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  Speaker 3: than a human arm. Does that mean it could feel
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  Speaker 3: details a person would miss with their forearm?
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  Speaker 2: In that specific metric, yes. Typical human arm skin is
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  Speaker 2: more like 30 to 50 millimeters for distinguishing two points.
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  Speaker 2: M arm gets down to 18.2, so finer than most
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  Speaker 2: of us on the arm itself.
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  Speaker 3: Interesting trade-off. We are better at the fingertips, but this
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  Speaker 3: arm has high-quality sensing all along its length.
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  Speaker 2: Which opens different kinds of uses. You can rest an
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  Speaker 2: object on the arm, or brush against it, or have
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  Speaker 2: a person interact with the middle of the arm, and
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  Speaker 2: the robot notices immediately.
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  Speaker 3: Have they shown it actually doing useful things with that,
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  Speaker 3: or is it mostly the sensing demonstration?
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  Speaker 2: They showed three scenarios. One is human interaction with those
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  Speaker 2: five touch types. Another is holding an object that is off-center,
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  Speaker 2: like a basketball placed not in the middle of the arm,
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  Speaker 2: and adjusting to keep it balanced.
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  Speaker 3: The arm itself is balancing a basketball by feeling the
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  Speaker 3: pressure distribution and shifting? Yes.
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  Speaker 2: It compares the live tactile features against a learned policy
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  Speaker 2: and commands the joints to stabilize it.
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  Speaker 3: That is more than just detecting touch. It is closing
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  Speaker 3: the loop into action.
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  Speaker 2: And the third one is the most interesting for safety.
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  Speaker 2: If something enters the path that the cameras cannot see,
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  Speaker 2: like a hand coming in from the side, the arm
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  Speaker 2: feels it through the skin, stops, figures out if the
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  Speaker 2: obstacle is temporary or staying, and then replans its trajectory
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  Speaker 2: around it.
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  Speaker 3: So even if vision is blocked, the arm can still
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  Speaker 3: protect itself and whatever it is working near.
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  Speaker 2: That is the claim, safe and flexible operation when you
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  Speaker 2: cannot see everything.
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  Speaker 3: I can see why people working on humanoid robots would
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  Speaker 3: care about this. The arms are constantly near people or clutter.
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  Speaker 2: They mention exactly that, extending the approach to legs, torsos,
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  Speaker 2: whole bodies, so that humanoids can interact more safely in
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  Speaker 2: homes or factories.
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  Speaker 3: The obstacle avoidance without vision is the part that sticks
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  Speaker 3: with me. Most systems panic or freeze if a camera
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  Speaker 3: is occluded.
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  Speaker 2: Here, the touch becomes the primary channel for that moment.
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  Speaker 2: It detects the contact, does an emergency stop, assesses whether
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  Speaker 2: the contact is fleeting or persistent, and then finds a
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  Speaker 2: new path.
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  Speaker 3: That assessment step is important. You do not want it
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  Speaker 3: to treat a momentary brush the same as a solid
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  Speaker 3: obstacle that is staying there.
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  Speaker 2: Right. And in the human interaction tests, the recognition of stroking, pinching, poking, tickling,
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  Speaker 2: and hitting was extremely high. They map those to emotional
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  Speaker 2: responses so the robot can signal back how it interprets
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  Speaker 2: the intent.
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  Speaker 3: I wonder how much of that emotional layer is for
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  Speaker 3: demonstration versus something they expect in real deployments. Signaling intent
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  Speaker 3: could help people feel more comfortable around the robot.
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  Speaker 2: It could. If a robot arm flinches or shows a
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  Speaker 2: clear reaction when touched unexpectedly, a human knows it registered
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  Speaker 2: the contact. That is useful feedback even without fancy faces.
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  Speaker 3: True. And the basketball example shows it can do contact-rich manipulation,
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  Speaker 3: not just avoid things, supporting an unbalanced load by adjusting
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  Speaker 3: posture based on distributed force. Yes.
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  Speaker 2: Traditional arms often need the object perfectly gripped or supported
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  Speaker 2: at known points. Here, the arm can use its whole
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  Speaker 2: surface as a sensing and reacting platform.
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  Speaker 3: How strong is the evidence that this is reliable? They
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  Speaker 3: mention 97.9% fidelity under interference, and 99.8% on the touch types,
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  Speaker 3: those are lab numbers.
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  Speaker 2: They are. The paper reports the controlled experiments, and the
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  Speaker 2: university release highlights the same figures. Real-world long-term reliability would
• 9:38
  Speaker 2: need more testing, as always. But the architecture is designed
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  Speaker 2: so the soft skin does not compromise the mechanical precision.
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  Speaker 3: That last point matters. Plenty of soft robots are great
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  Speaker 3: at sensing or grasping, but cannot carry real loads or
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  Speaker 3: move accurately. This one claims both.
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  Speaker 2: Pressure loads over 15 kilograms and torsional loads of about 7.35
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  Speaker 2: newton meters, with positioning accuracy matching conventional arms, so the
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  Speaker 2: skeleton stays rigid and capable.
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  Speaker 3: I keep coming back to the optical approach. By putting
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  Speaker 3: the cameras inside and watching markers or the deformation, they
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  Speaker 3: sidestep the usual problems of flexible electronics failing and or
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  Speaker 3: needing complicated interconnects.
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  Speaker 2: And it is inherently more resistant to the kinds of
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  Speaker 2: interference that plague some electronic skins. Light, temperature, humidity, electromagnetic fields,
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  Speaker 2: even sound. The sensing holds up.
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  Speaker 3: Sound? Acoustic interference affecting a touch sensor is not something
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  Speaker 3: I think about often, but factories are noisy.
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  Speaker 1: Yes.
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  Speaker 2: So the design choices seem aimed at practical deployment, not
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  Speaker 2: just a one-off lab demo.
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  Speaker 3: The team is from HKUST, and they have a robotics
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  Speaker 3: institute there. This feels like part of a broader push
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  Speaker 3: on embodied systems.
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  Speaker 2: They explicitly link it to embodied AI, systems that have bodies,
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  Speaker 2: sense the world through those bodies, and learn from the interaction,
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  Speaker 2: rather than pure software agents that only see images or text.
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  Speaker 3: Which is the hard part everyone talks about with humanoids.
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  Speaker 3: Language models got good at talking, physical intuition is still lagging.
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  Speaker 2: This kind of dense, reliable, tactile feedback on large surfaces
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  Speaker 2: could help close that gap for the arms at least.
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  Speaker 2: Stepping back, what strikes me is how this shifts the
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  Speaker 2: design philosophy. For a long time, robot arms have been
• 11:29
  Speaker 2: mostly about vision and precise end effectors. The body of
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  Speaker 2: the arm was just structure.
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  Speaker 3: Now the body becomes part of the sensorium. That changes
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  Speaker 3: what kinds of tasks become easier and what kinds of
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  Speaker 3: environments become safer.
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  Speaker 2: Home service, industrial assistance, places where people and robots share space.
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  Speaker 2: If the arm can feel a person nearby, even when
• 11:52
  Speaker 2: the cameras cannot see them, that reduces a whole class
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  Speaker 2: of accidents.
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  Speaker 3: It also makes more fluid collaboration possible. A person could
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  Speaker 3: guide the arm by touch, or the arm could support
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  Speaker 3: a shared load without needing perfect visual tracking.
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  Speaker 2: And because the sensing is whole arm, not just at
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  Speaker 2: the tip, those interactions can happen anywhere along the length.
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  Speaker 2: That is closer to how we use our own arms.
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  Speaker 3: I wonder about scaling it. Putting this on every segment
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  Speaker 3: of a humanoid would add weight and complexity. Do they
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  Speaker 3: talk about that?
• 12:23
  Speaker 2: They note it could be extended to legs and torsos.
• 12:26
  Speaker 2: The optical approach avoids a lot of wiring, which helps,
• 12:30
  Speaker 2: and the soft skin is integrated rather than bolted on
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  Speaker 2: after the fact. Still, full-body versions will need careful engineering
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  Speaker 2: for power, heat, and durability.
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  Speaker 3: Fair. But the principle is demonstrated. You can have high-resolution
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  Speaker 3: tactile sensing over large areas without destroying the mechanical performance.
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  Speaker 2: And the control loop is lightweight enough to run in
• 12:52
  Speaker 2: real time by extracting features instead of doing heavy end-to-end
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  Speaker 2: learning on every frame.
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  Speaker 3: That practicality is what makes it more than a curiosity.
• 13:01
  Speaker 3: Plenty of research skins never leave the lab because they
• 13:04
  Speaker 3: are too fragile or too slow or too hard to interpret.
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  Speaker 2: Here they have shown both the sensing performance and closed-loop behaviors.
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  Speaker 2: balancing an off-center object, recognizing intentional touches, and replanning around
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  Speaker 2: unseen obstacles.
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  Speaker 3: If this approach spreads, we might start seeing robot arms
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  Speaker 3: that feel less like machines and more like partners that
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  Speaker 3: notice when you brush against them.
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  Speaker 2: Or that can work in the dark or in dust
• 13:29
  Speaker 2: or when people step into their path unexpectedly and still adapt.
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  Speaker 3: It is a reminder that intelligence in the physical world
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  Speaker 3: is not only about bigger models— It is about better
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  Speaker 3: sensors and tighter loops between sensing and action.
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  Speaker 2: Embodied intelligence in the most literal sense. Body is not
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  Speaker 2: just a platform for the brain. The body is part
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  Speaker 2: of how the system knows the world.
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  Speaker 3: I like that framing. And the fact that a university
• 13:55
  Speaker 3: team published the details in a nature journal and is
• 13:58
  Speaker 3: now highlighting it publicly means other groups can build on
• 14:01
  Speaker 3: the ideas. Yes.
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  Speaker 2: Open science on this kind of hardware is valuable because
• 14:06
  Speaker 2: the physical design choices matter as much as the algorithms.
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  Speaker 3: Well, that is a solid place to leave it. A
• 14:13
  Speaker 3: robotic arm that can feel across its length, interpret the touch,
• 14:17
  Speaker 3: and act on it, all while keeping its strength and precision.
• 14:21
  Speaker 3: It is a concrete step toward robots that are more
• 14:24
  Speaker 3: at home in the messy real world.
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  Speaker 2: And toward systems where touch is no longer a luxury add-on,
• 14:30
  Speaker 2: but a core part of how they operate. Thanks for
• 14:32
  Speaker 2: thinking through it with me.
• 14:34
  Speaker 3: Always good to dig into these things. Until next time.
• 14:37
  Speaker 2: One thing I keep thinking about is how this compares
• 14:39
  Speaker 2: to other tactile robots we have seen. There have been
• 14:43
  Speaker 2: electronic skins and soft grippers, but few that cover the
• 14:46
  Speaker 2: entire arm while keeping industrial-level precision and load capacity.
• 14:50
  Speaker 3: Yeah, a lot of the impressive soft robots are great
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  Speaker 3: for delicate handling but fall apart if you ask them
• 14:56
  Speaker 3: to move quickly or carry weight. M-ARM seems to have
• 14:59
  Speaker 3: solved the integration problem by embedding the skin inside a
• 15:02
  Speaker 3: rigid structure rather than replacing the structure.
• 15:06
  Speaker 2: And the choice of optical sensing is smart for durability.
• 15:09
  Speaker 2: Flexible electronics can fatigue or fail at the connections. Cameras
• 15:14
  Speaker 2: watching from inside are more protected and easier to replace
• 15:17
  Speaker 2: or calibrate in principle.
• 15:18
  Speaker 1: Hmm.
• 15:19
  Speaker 3: I wonder about the lighting inside the arm. Do they
• 15:22
  Speaker 3: need special illumination so the cameras can see the skin
• 15:25
  Speaker 3: deformation clearly in all conditions?
• 15:27
  Speaker 2: The reports mention sub-millimeter scale cameras analyzing the distribution of forces.
• 15:33
  Speaker 2: They probably have controlled internal lighting or use markers that
• 15:36
  Speaker 2: are easy to track. The fact that it maintains high
• 15:39
  Speaker 2: fidelity under external light interference suggests the internal system is
• 15:43
  Speaker 2: well shielded.
• 15:44
  Speaker 3: That 97.9% under five types of interference is reassuring. It
• 15:49
  Speaker 3: means you could put this near welding equipment or in
• 15:52
  Speaker 3: a humid kitchen and the sensing should still work. Exactly.
• 15:56
  Speaker 2: And for the emotional responses, even if they are mainly
• 15:59
  Speaker 2: for the demo... The underlying classification of touch types is
• 16:03
  Speaker 2: the useful part. Knowing the difference between an accidental bump
• 16:06
  Speaker 2: and an intentional stroke lets the robot decide whether to stop, yield,
• 16:11
  Speaker 2: or continue.
• 16:12
  Speaker 3: That decision layer is what turns raw sensing into intelligence.
• 16:16
  Speaker 3: Without it, you just have a very sensitive alarm system.
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  Speaker 2: And they achieved it without needing massive datasets for end-to-end training.
• 16:23
  Speaker 2: By extracting those four or five features— location, area, force, number
• 16:28
  Speaker 2: of contacts— and feeding them to a policy, they keep
• 16:32
  Speaker 2: the computation light.
• 16:33
  Speaker 3: Which is crucial for running on the robot itself rather
• 16:36
  Speaker 3: than in the cloud. Latency matters when you are about
• 16:39
  Speaker 3: to hit something.
• 16:41
  Speaker 2: Speaking of latency, the system responds in real time. The
• 16:45
  Speaker 2: perception action loop is closed tightly enough for the basketball
• 16:48
  Speaker 2: balancing and the obstacle replanning to look natural.
• 16:52
  Speaker 3: I would love to see video of the basketball, an
• 16:54
  Speaker 3: arm gently shifting under a ball that is sliding toward
• 16:57
  Speaker 3: the edge, just from feeling the pressure change. That is
• 17:00
  Speaker 3: the kind of quiet competence that makes robots useful.
• 17:04
  Speaker 2: And the fact that it works when the object is
• 17:06
  Speaker 2: off-center means it is not relying on a single force
• 17:09
  Speaker 2: torque sensor at the wrist. The distributed sensing gives it
• 17:12
  Speaker 2: a richer picture of the load.
• 17:14
  Speaker 3: Which could be useful for carrying irregular objects or supporting
• 17:17
  Speaker 3: a person who is leaning on the arm. The robot
• 17:20
  Speaker 3: would feel the shift and adjust automatically.
• 17:23
  Speaker 2: That is one of the home and medical assistance scenarios
• 17:26
  Speaker 2: they mention. Safer physical collaboration.
• 17:29
  Speaker 3: It also raises interesting questions about privacy and consent. If
• 17:33
  Speaker 3: a robot can feel detailed touch patterns all over its
• 17:36
  Speaker 3: arm and classify them, that is a lot of information
• 17:39
  Speaker 3: about how people interact with it.
• 17:41
  Speaker 2: True. In a home setting, you would want clear boundaries
• 17:44
  Speaker 2: on what gets recorded or acted upon. The current system
• 17:48
  Speaker 2: seems focused on immediate response rather than long-term logging. But
• 17:52
  Speaker 2: those design choices will matter as these systems spread.
• 17:55
  Speaker 3: Good point. Technology that can sense this finely needs thoughtful
• 17:59
  Speaker 3: rules around it. Looking at the bigger picture, this work
• 18:02
  Speaker 3: arrives at a moment when a lot of groups are
• 18:04
  Speaker 3: trying to give robots better physical intuition. Language models handle
• 18:09
  Speaker 3: words well, but the physical world is still hard.
• 18:12
  Speaker 2: And touch is one of the richest channels we have
• 18:15
  Speaker 2: as humans. We use it constantly without thinking. Robots that
• 18:19
  Speaker 2: lack it are missing a huge part of how to
• 18:21
  Speaker 2: navigate shared spaces.
• 18:23
  Speaker 3: The M-arm approach of putting high-quality sensing on the arm itself,
• 18:27
  Speaker 3: rather than only at the end effector, feels like a
• 18:30
  Speaker 3: logical next step. Most contact does not happen at the fingertips.
• 18:35
  Speaker 1: Exactly.
• 18:36
  Speaker 2: When you carry a box or lean on a railing
• 18:38
  Speaker 2: or brush past someone, a lot of the information comes
• 18:41
  Speaker 2: from the forearm and upper arm. Giving robots that capability
• 18:44
  Speaker 2: makes their interactions more human-like in a useful way.
• 18:49
  Speaker 3: I also appreciate that they published the methods and the
• 18:51
  Speaker 3: performance numbers. Other labs can try to reproduce the optical
• 18:55
  Speaker 3: skin design or the feature extraction control loop.
• 18:59
  Speaker 2: And the university is actively talking about it now, which
• 19:02
  Speaker 2: helps the ideas travel beyond the academic paper. Nature sensors
• 19:06
  Speaker 2: is a good venue, but the public release makes it accessible.
• 19:10
  Speaker 3: Do you think we will see commercial versions of something
• 19:12
  Speaker 3: like this in the next few years, or is it
• 19:15
  Speaker 3: still research?
• 19:16
  Speaker 2: Hard to say. The mechanical integration looks promising, and the
• 19:20
  Speaker 2: control is lightweight. But manufacturing the soft skins at scale,
• 19:24
  Speaker 2: ensuring long-term durability, and integrating with existing robot platforms will
• 19:29
  Speaker 2: take engineering effort. It is closer than a pure laboratory curiosity.
• 19:34
  Speaker 3: The load capacity and precision numbers help. If it can
• 19:37
  Speaker 3: do what conventional arms do and also feel, companies might
• 19:41
  Speaker 3: be willing to try it.
• 19:42
  Speaker 2: Especially in applications where safety around people is a selling point.
• 19:46
  Speaker 2: Collaborative robots already exist, but most still rely heavily on
• 19:50
  Speaker 2: vision and limited force sensing. Whole-arm touch could be a differentiator.
• 19:55
  Speaker 3: And for research in embodied AI, having a platform like
• 19:59
  Speaker 3: this lets people test whether better sensory data actually improves
• 20:02
  Speaker 3: learning and adaptation, not just reactive control. Yes.
• 20:07
  Speaker 2: The closed-loop behaviors they showed are rule-based or policy-based on features,
• 20:11
  Speaker 2: but the same hardware could support richer learning if someone
• 20:14
  Speaker 2: wanted to go further.
• 20:16
  Speaker 3: It is a nice example of hardware and software meeting
• 20:19
  Speaker 3: in the middle. The sensing makes the control simpler, and
• 20:23
  Speaker 3: the control makes the sensing useful.
• 20:25
  Speaker 2: That is often where progress happens. Not a single breakthrough,
• 20:29
  Speaker 2: but a well-executed combination that removes previous obstacles.
• 20:34
  Speaker 3: So to sum it up for anyone just joining, Researchers
• 20:37
  Speaker 3: in Hong Kong have built a robotic arm with soft
• 20:39
  Speaker 3: optical skin covering its upper and lower arms. It can
• 20:43
  Speaker 3: feel contact with high resolution, interpret different kinds of touch,
• 20:47
  Speaker 3: balance objects using that feedback, and avoid obstacles it cannot see.
• 20:52
  Speaker 3: And it does all that without losing the strength or
• 20:54
  Speaker 3: accuracy of a normal industrial arm.
• 20:57
  Speaker 2: A concrete step toward robots that are more aware of
• 20:59
  Speaker 2: the physical world through their own bodies. It is the
• 21:02
  Speaker 2: kind of quiet, practical advance that can add up over time.
• 21:06
  Speaker 3: I am glad we spent time with it. These details
• 21:09
  Speaker 3: matter more than the headlines sometimes. Agreed.
• 21:13
  Speaker 2: Thanks for exploring it with me.