Cross-Embodiment Motion Planning by Coupling Body-Agnostic World Model with Differentiable Ego-Dynamics
Overview
This research presents Hourglass, an hourglass-shaped architecture designed to solve motion planning in cluttered environments across different robot embodiments. By factorizing the problem into a body-agnostic world model and per-robot differentiable ego-dynamics, the method pools data from diverse robots while maintaining physical fidelity. The authors report that their generalist policy outperforms imitation-learning and diffusion-policy baselines by 3.8--10.6 percentage points in success rate on seven simulated robots of five locomotion types. Additionally, after three minutes of teleoperated driving for parameter identification, the model achieves zero-shot transfer to a real skid-steer robot unseen during training, successfully navigating indoor scenes.
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Environment: simulation
“orld model, and imagination training through each robot's template makes the policy body-aware. On seven simulated robots of five locomotion types, the generalist policy outperforms imitation-learning and diffusion-policy baselines on the same world model by 3.8--10.6 percentage points in success rate and matches per-robot specialists without dedicated training, while approaching its parameter-conditioned MPPI teache”
Exact source · revision 1Source owner not reported
Control: teleoperated
“r within 1.4--2.8 points without an online map. After three minutes of teleoperated driving for identification, the synthetic-data-trained model transfers zero-shot to a real skid-steer robot unseen in training, navigating unseen indoor scenes.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported duration: 3 other · Basis not reported
“r within 1.4--2.8 points without an online map. After three minutes of teleoperated driving for identification, the synthetic-data-trained model transfers zero-shot to a real skid-steer robot unseen in training, navigating unseen indoor scenes.”
Exact source · revision 1Source owner not reported
Reported quantity · locomotion types covered: 5 other · Basis not reported
“orld model, and imagination training through each robot's template makes the policy body-aware. On seven simulated robots of five locomotion types, the generalist policy outperforms imitation-learning and diffusion-policy baselines on the same world model by 3.8--10.6 percentage points in success rate and matches per-robot specialists without dedicated training, while approaching its parameter-conditioned MPPI teache”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalCross-Embodiment Motion Planning by Coupling Body-Agnostic World Model with Differentiable Ego-Dynamics
This research presents Hourglass, an hourglass-shaped architecture designed to solve motion planning in cluttered environments across different robot embodiments. By factorizing the problem into a body-agnostic world model and per-robot differentiable ego-dynamics, the method pools data from diverse robots while maintaining physical fidelity. The authors report that their generalist policy outperforms imitation-learning and diffusion-policy baselines by 3.8--10.6 percentage points in success rate on seven simulated robots of five locomotion types. Additionally, after three minutes of teleoperated driving for parameter identification, the model achieves zero-shot transfer to a real skid-steer robot unseen during training, successfully navigating indoor scenes.
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