# Chris Paxton
Principales episodios de podcast
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Top podcast episodes featuring chris paxton.
Ep#95: Action-to-Action Flow Matching
Diffusion Policy was one of the big breakthroughs that has enabled an explosion in real-world robot learning. However, it’s always had a weakness, which is that it works by computing a final action trajectory from random noise, which leads to high latency when predicting a final action sequence.
In…
Ep#97: Cross-Embodiment Robot Manipulation via a Unified Hand Action Space
Representing actions for robot hands is actually very challenging. Robot hands have different sizes and shapes, and different degrees of freedom. Representing their actions purely as joint states — as we naively would — leads to less transfer across the wide variety of available hands. Instead, cou…
Ep#99: DYNA-2: A 1 Million Hour Scaling Law for World-Action Models
Excitement about world-action models and robot learning has never been higher — they promise a way to use human egocentric data to train massive robotics models which can provide the “GPT” moment for robotics and unlock general-purpose embodied intelligence. And yet there’s been little concrete dem…
Ep#100: 100 Episodes of Embodied AI
Embodied AI is exploding, and it’s been amazing to learn about it with you all.
We have now recorded over a hundred discussions with the best researchers in the world on embodied artificial intelligence and robotics, covering topics like scaling robot learning, humanoid whole body control, tactile …
Ep#104: Light-Loco-Parkour: Versatile Perceptive Whole-Body Locomotion via Multi-Skill Distillation
One of the key advantages of legged robots like humanoids should be how effectively they can move across a wide variety of terrain types to accomplish their task. But Light-Loco-Parkour from the team at Light Origins aims to change that: using only onboard sensing, they show a policy which can deci…
Ep#105: Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning
Imitation learning, especially with interventions, has driven so much recent robotics progress. However, improving a policy via targeted interventions until it reaches a useful and deployable success rate is a time and labor intensive process. Instead, wouldn’t it be great if policies could improve…
Ep#107: Do As I Do: Dexterous Manipulation Data from Everyday Human Videos
Achieving human-like dexterity is the next frontier for robotics, and yet dexterity data is often subtly hard to scale. Real-world dexterity data, including things like finger-pose estimates, is often slightly off, making it physically invalid and hard to execute on real hardware and hard to learn …
Ep#109: Enigma - Connecting 100 Robots To The Internet
What happens if you connect a hundred AI-powered robots to the internet and just let people give them any instructions they want? Enigma founder Jonathan Jacobi joins us to talk about his experience deploying a fleet of robots on the internet, keeping them operational, and watching people truly tes…
Ep#110: ENPIRE: Agentic Robot PolicySelf-Improvement in the Real World
In many fields, automated research has become increasingly plausible with the improving capabilities of frontier language models, and the field robotics may be no exception. In ENPIRE, Wenli Xiao, Jia Xie, and Tonghe Zhang build a harness framework that captures physical feedback and allows for aut…
Ep#113: MolmoBOT: Large-Scale Simulation Enables Zero-Shot Manipulation
Using simulation to teach robots would unblock the massive scale of robotics data that seems to be missing right now. The issue is usually sim-to-real transfer; MolmoBOT aims to solve this with sufficient diversity of simulation data, so that no real data is necessary for robot deployment. It has …
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