1 citations · 1 across the 7 of their papers we have counts for
8 papers
Mulligan: Performance-Guided Data Collection for Efficient On-Robot Learning
Lars Ankile, Perry Dong, Rohan Bhowmik +4
Learning from human demonstrations is a reliable way to teach robots new tasks, but the gains from each additional demonstration shrink as the policy improves. Continued improvemen…
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…
From Imitation to Refinement -- Residual RL for Precise Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld +2
Recent advances in Behavior Cloning (BC) have made it easy to teach robots new tasks. However, we find that the ease of teaching comes at the cost of unreliable performance that sa…
JUICER: Data-Efficient Imitation Learning for Robotic Assembly
Lars Ankile, Anthony Simeonov, Idan Shenfeld +1
While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requ…