Vision-language-action (VLA) and world-action models typically absorb unfamiliar manipulation tasks through additional robot data collection and policy optimization. This recurring retraining burden slows the acquisition of new behavior. We present Teach-and-Grow Learning (TGL), a training-free architecture that turns...
Chang Nie, Zhe Liu, He-Sheng Wang· The international journal of...· 2 citations
The massive overactuation in the human musculoskeletal system makes it challenging to train musculoskeletal models to generate human-like motion via reinforcement learning, primarily because exploration in the resulting high-dimensional and redundant action space is extremely inefficient. To address this problem, we pr...
Jun Hyuk Lee, Chihyeong Lee, Jooeun Ahn· 1 citation
VLCP closes the loop where the failure actually lives, on the control code, within a single episode, and keeps the VLM frozen, which is a training-free policy with a tenfold gap between pooled success and confidence intervals in every scene family.
Dhia Naouali, Ming Wu, Claudia Wong et al.· 0 citations
Instant Episode Repetition (IER) is introduced, a simple and novel mechanism that improves sample efficiency by immediately repeating action sequences from successful episodes during environment interaction by directly influences the data collection process.
Hoda Yamani, Yuning Xing, Koen van Rijnsoever et al.· 0 citations
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Large language model (LLM)-driven embodied agents rely on environment states to interpret scenes, generate high-level plans, and drive physical execution, making planner-visible state representations a critical security boundary. Existing attacks primarily manipulate user instructions, prompt contexts, model behavior,...
Jiawei Liu, Jiacheng Guo, Tian Zhang et al.· 0 citations
A neurosymbolic agent that factors long-horizon household tasks into task-directed visual exploration and constrained symbolic planning and evaluates executable continuations using a domain-independent planning heuristic is presented.
Mohammad Albinhassan, Yuming Feng, Alessandra Russo et al.· 0 citations
Dynamics-effective is used to describe an intervention that changes the model's future computation in a sustained and target-specific way under autonomous rollout under autonomous rollout.
This work proposes BrainWAM, a structured action-space coordination framework that converts semantic reasoning and predictive world modeling into two specialized action-oriented pathways, and aligns them at the level of compact action representations, and introduces an asynchronous rectified-flow inference strategy wit...
Bing Zhan, Shu-Yao Shang, Shuo Lu et al.· 1 citation
This work considers rectangular 2D grids, where uniform-sized loads are first stored, up to full capacity, and subsequently retrieved according to prescribed arrival and departure sequences, and develops an online prioritized multi-agent path planning algorithm for this problem.
William Zhang, Tzvika Geft, Jingjin Yu et al.· 0 citations
Mask2Real-WM is presented, a two-stage action-conditioned world model for dexterous manipulation that decouples pixel prediction into a dynamics model and a rendering model that shows that mask conditioning and simulation pretraining are both required for per-DoF action controllability across all 23 degrees of freedom.
Riccardo Feingold, Davide Liconti, Chenyu Yang et al.· 1 citation
This work proposes COMPACT-VA, a planning-aligned working memory framework built on conditional VQ-VAE, compressing extended context into bounded representations, and evaluates on high-signal dynamic scenarios where historical context is most critical for behavior correctness, and accordingly design behavioral metrics.
Zhixuan Liang, Yuxiao Chen, Yurong You et al.· arXiv.org· 1 citation
We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF) robot embodiments that can solve long-horizon tasks directly from raw visuo-tactile perception. ADEPT pretrains a dexterou...
Jayjun Lee, Jessica Yin, Asif Rana et al.· 2 citations
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
