The study evaluated 281 imitation‑learning policies across seven families in 1,700 real‑robot trials of RJ45 connector grasping and insertion with a five‑fingered hand. Controlled ablations revealed that observation and action representations—such as ROI cropping, robot‑base framing, and point‑encoder normalization for point‑cloud policies, and absolute action prediction for vision‑language‑action policies—drastically improved success rates. Among 67 offline metrics, only five measures of predicted‑motion smoothness and directness correlated reliably with real‑world success, indicating that offline metrics can help shortlist policies but cannot replace actual robot testing.
By Ammar N. Abbas, Aayush Jain, Prashanth Viswanath, Nivesh Gadipudi, Giulia Vilone, Jim Gibbons
arXiv:2605.18727v2 Announce Type: replace-cross
Abstract: Evaluating embodied systems with real dexterous hardware requires more than isolated motor-skill tests: an agent must perceive a changing sce...
By Feng Chen, Tianzhe Chu, Li Sun, Pei Zhou, Zhuxiu Xu, Shenghua Gao, Yuexiang Zhai, Yanchao Yang, Yi Ma
arXiv:2610.10388v1 Announce Type: cross
Abstract: Recent advances in multimodal foundation models have made them capable generalist physical agents for a range of manipulation tasks. However, success...
By Liu Renhang, Navonil Majumder, Tej Deep Pala, Soujanya Poria
EgoSpeedUp is a framework that transfers human manipulation tempo to robot policies by aligning and retiming robot demonstrations using phase-wise tempo estimates derived from human demonstrations. The method improves task success rates by an average of 25 percentage points and reduces successful execution time by 36.5% on two real-world manipulation tasks. It demonstrates that human manipulation tempo can serve as an effective temporal reference for faster and more reliable robot policies.
By Hanbit Oh, Yukiyasu Domae, Takuma Yagi
arXiv:2609.08123v1 Announce Type: cross
Abstract: A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly t...
By Suyog Khanal, Arun Kumar A V, Santu Rana
The paper introduces a learned speed prior that predicts a distribution over execution speed for robot demonstrations, allowing consistent retiming across tasks. By applying this prior to both robot teleoperation and handheld demonstrations, the authors retime demonstrations to a handheld pace before fine‑tuning a vision‑language‑action policy. Experiments on 120 RoboLab tasks and a real robot show that policies trained with the learned clocks achieve higher success rates and smoother motion, outperforming policies fine‑tuned on original or uniformly accelerated timings.
By Seungyong Moon, Deokjae Lee, Hansang Lee, Munkhtselmeg Battsengel, Youngin Kim, Seonghyeon Yun, Sungjin Jeon, SongEun Kim, Jeongmin Park, Hyun Oh Song
arXiv:2603. 15956v3 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data.
By Zifan Xu, Ran Gong, Maria Vittoria Minniti, Kausik Sivakumar, Ahmet Salih Gundogdu, Eric Rosen, Riedana Yan, Tushar Kusnur, Zixing Wang, Di Deng, Peter Stone, Xiaohan Zhang, Karl Schmeckpeper
arXiv:2608. 16889v1 Announce Type: cross Abstract: Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task.
By Bingxin Xu, Yuzhang Shang, Emilio Ferrara
The paper introduces Orchestra, a robotic agent that separates task planning from action generation to follow multimodal, long‑horizon instructions. Orchestra decomposes instructions into ordered subtasks, tracks progress, updates object locations, and uses a progress evaluator to decide when to advance. On the new RMBench‑Compose benchmark, Orchestra outperforms prior methods, achieving higher success rates and longer average subtask sequences, and shows improved performance on real‑world multi‑step tasks.
By Youpeng Wen, Gang Wang, Tianyu Huang, Weiwei Zhang, Zhijian Huang, Wei Chen, Xieyuanli Chen, Yunhui Liu
Long-horizon robot manipulation chains many contact-rich skills into one multi-stage task. Vision-language-action (VLA) models increasingly master the individual skills, yet the chain still fails: err...
arXiv:2608. 07065v1 Announce Type: cross Abstract: Action-chunking visuomotor policies learn from demonstrations and improve temporal consistency by predicting short action sequences rather than single-step commands.
By Jinhe Tang, Weiming Zhi
arXiv:2610.17709v1 Announce Type: cross
Abstract: Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the data...
By Yusuf Umut Ciftci, Joonwon Kang, Ajay Suresha Sathya, Somil Bansal