Papers

Filtered to teleoperation · clear filter

Browse by term

continual learning 64reinforcement learning 34large language models 17benchmarking 12vision-language models 10generative models 8language models 8video generation 7multimodal models 6natural language processing 6robotics 6world models 6benchmarks 5diffusion models 5on-policy distillation 5policy optimization 5scalability 5self-distillation 5vision-language-action models 5autoregressive models 4computer vision 4diffusion transformers 4LLMs 4multimodal large language models 4verifiable rewards 4attention mechanisms 3embodied intelligence 3image editing 3long-term memory 3multimodal learning 3

Matching papers

HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone

141 upvotes · 28 JUL 2026 · Simple AI, Yuteng Wei, Jinming Ma et al.

This paper develops a system that allows robots to learn manipulation policies using high-fidelity data without the need for real-robot teleoperation, which is expensive to scale. Practitioners can use this approach to train robots for tasks like precision insertion with high accuracy and success rates.

robot learningUMI (unstable manipulation)manipulation policieshigh-fidelity datateleoperation