5 papers · 1 filter
Residual Off-Policy RL for Finetuning Behavior Cloning Policies
Lars Ankile, Zhenyu Jiang, Rocky Duan +3
Recent advances in behavior cloning (BC) have enabled impressive visuomotor control policies. However, these approaches are limited by the quality of human demonstrations, the manu…
Bridging the Sim2Real Gap: Vision Encoder Pre-Training for Visuomotor Policy Transfer
Yash Yardi, Samuel Biruduganti, Lars Ankile
Simulation offers a scalable and efficient alternative to real-world data collection for learning visuomotor robotic policies. However, the simulation-to-reality, or Sim2Real distr…
Robot Learning with Super-Linear Scaling
Marcel Torne, Arhan Jain, Jiayi Yuan +5
Scaling robot learning requires data collection pipelines that scale favorably with human effort. In this work, we propose Crowdsourcing and Amortizing Human Effort for Real-to-Sim…
DexHub and DART: Towards Internet Scale Robot Data Collection
Younghyo Park, Jagdeep Singh Bhatia, Lars Ankile +1
The quest to build a generalist robotic system is impeded by the scarcity of diverse and high-quality data. While real-world data collection effort exist, requirements for robot ha…
Diffusion Policy Policy Optimization
Allen Z. Ren, Justin Lidard, Lars L. Ankile +6
We introduce Diffusion Policy Policy Optimization, DPPO, an algorithmic framework including best practices for fine-tuning diffusion-based policies (e.g. Diffusion Policy) in conti…