manipulation

1
From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Intervention arxiv.org
The paper presents PARTS, a framework that augments a frozen pretrained robot policy with RL‑learned residuals on selected bottleneck subtasks, using local success rewards and minimal human resets. In real‑world bimanual and single‑arm tasks, PARTS more than doubles success rates with only minutes of robot rollouts, outperforming prior fine‑tuning methods.
Hugging Face Daily Papersarxiv.orgSichang Su, Benjamin Yang, Zhiyun Deng4d1 minpaper
2
Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand arxiv.org
The authors train an anthropomorphic robotic hand to crawl, steer, and recover from falls using its fingers for both support and manipulation, via a reinforcement‑learning reward formulation tuned to the hand's asymmetry. Sim‑to‑real experiments show faster locomotion than quadruped‑style rewards and successful untethered tasks without onboard vision.
Hugging Face Daily Papersarxiv.orgAmirhossein Kazemipour, Hehui Zheng, Robert Katzschmann7d1 minpaper

From Pretraining to Proficiency: Real-World Subtask RL for Long-Horizon Manipulation with Minimal Human Interventionarxiv.org