Papers

Filtered to robotics · clear filter

Browse by term

continual learning 83reinforcement learning 50large language models 12benchmarking 11benchmarks 11language models 11vision-language models 11robotics 7natural language processing 6world models 6generative models 5recursive self-improvement 5attention mechanisms 4diffusion Transformers 4multi-agent systems 4multimodal learning 4multimodal models 4on-policy distillation 4self-distillation 4self-supervised learning 4transformers 4video generation 4vision-language-action models 4agent-based systems 3agentic models 3agentic search 3autonomous systems 3coding agents 3diffusion models 3image generation 3

Matching papers

PhysBrain 1.5: From Vision-Language Models to Physical Foundation Models

172 upvotes · 14 SEP 2026 · DeepCybo Team, Yu Bin, Haipeng Cao et al.

This paper develops a unified model that can understand physical environments, generate actions, and predict future states, using a combination of vision, language, and embodied interactions. Practitioners may care about this model because it could be used to create robots or other agents that can interact with and adapt to their physical surroundings.

vision-language modelsphysical foundation modelsembodied AImultimodal learningrobotics

Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence

142 upvotes · 17 AUG 2026 · Xin Ding, Liang Mi, Mingzhe Huang et al.

This paper introduces Zetta, a system that enables embodied agents to learn and adapt in real-time while executing physical tasks, allowing for more efficient and effective learning. Practitioners in robotics and AI may care about Zetta's approach as it could lead to more reliable and scalable physical intelligence.

embodied agentsclosed-loop learningphysical intelligencereinforcement learningrobotics

TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM

118 upvotes · 29 JUL 2026 · Hengyi Xie, Chenfei Yao, Xianjin Wu et al.

This paper introduces TurboVLA, a new vision-language-action model that reduces computation and memory overhead by directly exchanging information between visual observations and language instructions, allowing for faster and more efficient robotic manipulation. Practitioners might care about this approach for building more efficient and effective VLA models.

vision-language-action modelscontinual learningroboticsagentslightweight models

Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models

62 upvotes · 11 SEP 2026 · Jianman Lin, Shailesh Shailesh, Zhongyi Luo et al.

This paper proposes a method called Latent Interface Training (LIT) to improve the generalization of robotics foundation models by preventing them from relying on visual shortcuts when learning to generate actions from pre-trained visual representations. This is important for robots to perform well in new, unseen environments.

continual learningroboticsvision-action modelslatent interfacesaction generationvisual conditioning

Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

59 upvotes · 16 JUL 2026 · Xiaomi Robotics Team, Jun Guo, Piaopiao Jin et al.

This paper introduces a vision-language-action model that can perform mobile manipulation tasks in unseen environments with minimal training data, and how it can be scaled up to achieve better performance. Practitioners might care about this model for building robots that can adapt to new tasks with minimal fine-tuning.

vision-language-action modelscontinual learningroboticsaction generationtrajectory dataauto-labeling

Progress Reward Modeling for Robotic Learning: A Comprehensive Survey

55 upvotes · 22 JUL 2026 · Jianshu Zhang, Keliang Wu, Haoran Lu et al.

This paper provides a comprehensive survey of progress reward modeling in robotic learning, aiming to bridge the gap in the field by offering a unified framework for understanding progress rewards. Practitioners in robotics and AI can care about this paper because it helps them understand the different approaches to progress rewards and how to evaluate their effectiveness.

robotic learningprogress rewardsreinforcement learningcontrolroboticstask execution