Tag
RL can assist robot data collection during demonstrations through a technique called Data Inoculation, improving data quality for both behavior cloning and RL, as shown in a new paper on stacking cubes.
A new blog post traces the evolution of Vision-Language-Action (VLA) models from RT-1 to MolmoAct2, explaining why VLAs are a leading bet for generalizable robot learning and covering key architectural trade-offs.
WorldBridge introduces a World Action Model with separate video and action experts connected via structured attention and trained with joint flow matching, achieving state-of-the-art task generalization on robot manipulation benchmarks by combining broad video coverage with sparse action supervision.
YC Paper Club is expanding to Cambridge, MA with its inaugural event on November 18, focusing on the state of the art in robot learning and control for academics and industry researchers.
The paper introduces Quality-Controllable Policy (QCP), enabling inference-time control over robot execution quality attributes like speed and smoothness via an iterative hindsight relabeling loop that fine-tunes on online rollouts, improving success rates and quality on Robomimic, RoboEval, and real-world manipulation tasks.
HuGe 提出了一族人形通用视觉-语言-动作（VLA）模型，利用超过2000小时的第一人称人类与机器人数据进行预训练，并通过伪本体感知与视觉适配实现全身控制，在 HumanoidArena 仿真基准上达到 SOTA，同时在 Unitree G1 实体机器人上验证了其有效性。
Eka Robotics 筹集 1.5 亿美元 A 轮融资，估值达 14.5 亿美元，其基于视觉-力-动作（vision-force-action）架构的物理 AI 模型让机器人灵巧抓取表现超越人类水平，引起机器人学界广泛关注。
Maniformer 的新论文提出 MEgoVista，一套基于头戴式装置的物理 AI 数据系统，通过立体视觉和全序列优化，将手部手指误差降至 4.29 毫米、头部跟踪误差控制在 0.65–2.83 毫米，并经 26 相机动作捕捉实验室验证，为机器人学习提供可测量的人类动作数据。
Reflex提出一个三阶段框架，让类人机器人通过自我中心RGB-D视觉实现对抛掷物体的全身心反射式接取控制，在仿真中达到85.7%成功率，并在真实Unitree G1机器人上以2.5米距离实现65%的接取成功率。
InterMimicGen 提出一个自我进化的运动模仿框架，通过数据飞轮将稀疏的人-物交互动作捕捉数据转化为持续扩展的、可执行的人形机器人全身 loco-manipulation 动作资源，并实现向真实机器人的迁移。
HumanoidToolBench 提出一个18项任务的基准，评估人形机器人从工具选择到移动执行的能力，并发布包含3.1k条演示的 ToolBook 数据集（仿真与真实 Unitree G1 采集），揭示了工具选择与任务成功执行之间的显著差距。
RLE-Bench introduces a qualifying-exam-style benchmark that evaluates coding agents' broader engineering capabilities in robotics — covering interactive control, policy learning, perception and estimation, and mechanical design — aggregating task-specific metrics into an overall RLE Index for systematic comparison.
PRISM is a real-to-sim-to-real framework that amplifies a few real human-object interaction videos into hundreds of diverse counterfactual videos via V2V generation, then reconstructs physically plausible motions to train a generalizable humanoid loco-manipulation policy deployed on a real robot without real-world fine-tuning.
Real2Gym is an agentic Real2Sim2Real framework that transforms human and robot video demonstrations into interactive simulation gyms, enabling skill transfer to physical robots with improved success rates over baselines.
EVO-WAM is a framework that adapts world action models to unseen robotics tasks by learning from generated video-action trajectories, significantly improving success rates without additional expert demonstrations.
This paper introduces Agent Priors-guided Policy Learning (APPL), a framework where construction and runtime agents use structural priors—describing what a behavior depends on—as an interface between skill learning and skill composition, improving out-of-distribution skill generalization on MetaWorld and long-horizon ManiSkill tasks.
A robotic hand is trained using reinforcement learning to perform self-supported locomotion on its fingertips, along with tasks like fall recovery, keyboard pressing, and object pushing, as presented in a research paper from ETH Zurich.
Gen-HumanEgo is a new dataset released by GenrobotAI for robot learning, featuring over 1,800 hours of egocentric human data with 10K+ unique tasks and synchronized camera views including 3D hand keypoints and structured annotations.
DeltaWAM introduces delta-based world-action models for bimanual manipulation, enhancing efficiency and performance by predicting visual changes and actions. It demonstrates improved success rates and reduced computational overhead.
This paper introduces GPT-Policy, a framework for in-context robot learning using vision-language models, enabling robots to learn from demonstrations without gradient updates. It evaluates the framework in real-robot trials, showing improved task completion.