arXiv Machine Learning
Behavioral Cloning Mystery
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DemoStream: Batched Task-and-Motion-Plan Optimization for Automated Learning from Demonstration
DemoStream is a Task and Motion Planning (TAMP) system that uses GPU‑accelerated batched sampling to generate high‑quality demonstrations for robotic manipulation. By optimizing demonstration quality and exploring many solutions in parallel, it improves behavior cloning success rates and enables real‑world demo collection at an average of 66 demos per hour. The approach evaluates different action spaces, cost functions, and batch sizes in simulation to understand their impact on downstream policy performance.
arXiv AI
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Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation
Sim-and-Human Co-training (SimHum) is a method that combines simulation and human demonstration data to train bimanual manipulation policies. It first extracts kinematic priors from simulation and visual priors from human observations, then fine‑tunes on a small real‑robot dataset. With only 80 real‑robot episodes per task, SimHum achieves 62.5% success on out‑of‑distribution scenes across four tabletop tasks, outperforming real‑only training by 53.7% and improving the best single‑source baseline by 35.0% in a matched‑time study.
arXiv AI
Jun 2
When Does Predictive Inverse Dynamics Outperform Behavior Cloning?
arXiv:2601. 21718v2 Announce Type: replace-cross Abstract: Behavior cloning (BC) is a practical offline imitation learning method, but it often fails when expert demonstrations are limited.
arXiv AI
Jul 13
ReinforceGen: Hybrid Skill Policies with Automated Data Generation and Reinforcement Learning
arXiv:2512. 16861v2 Announce Type: replace-cross Abstract: Long-horizon manipulation has been a long-standing challenge in the robotics community.
arXiv Machine Learning
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DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy
arXiv:2506. 20668v3 Announce Type: replace-cross Abstract: We propose DemoDiffusion, a simple method for enabling robots to perform manipulation tasks by imitating a single human demonstration, without requiring task-specific training or paired human-robot data.
arXiv Machine Learning
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Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning
arXiv:2608.21204v1 Announce Type: cross Abstract: Behaviour Cloning (BC) has driven remarkable progress in robot manipulation, yet it is fundamentally limited by its inability to self-improve: a poli...