Predictive world models enable robots to plan by imagining the outcomes of their actions, but their value for control hinges on generating many rollouts quickly. This creates a bottleneck for diffusion-based world models: multistep sampling makes each rollout expensive, limiting large-scale action search at inference t...
Susie Lu, Haonan Chen, Weirui Ye et al.· arXiv.org· 0 citations
Physical human-robot interaction requires yielding transiently to contact yet recovering the commanded reference under sustained load. Finite-stiffness impedance control retains a static deflection there, while predictive alternatives typically optimize a nonlinear robot or impedance model online. Operational-space can...
This work proposes an audio system that can be used for both an attentive listening system with the android ERICA and a conversation support system with mobile Teleco robots, with one of them acting as an avatar for a remote operator.
Divesh Lala, Yogeeswaran Muthukumaran, Vincent Fernandes et al.· 0 citations
Facet-0 unifies multimodal representation learning and reinforcement learning post-training around a joint action-wrench proposal, which reaches 82% mean success on five sub-millimeter computer-assembly tasks, compared with 15% for the strongest baseline.
Hao-Yuan Deng, Hai-Chao Liu, Wen-Kai Guo et al.· 1 citation
Reach audiences
Advertise in front of researchers, engineers, and readers.
Equal nominal task success does not imply preservation of expert performance across execution speeds, and an expert and learner under the same task conditions, initial-condition draws, and speedup factors is compared.
Clinton Enwerem, John S. Baras, C. Belta· 0 citations
Solver-Gradient Guided Reinforcement Learning is proposed, a solver-sensitivity augmentation for RL-based online MPC cost-weight adaptation that reaches PPO's best closed-loop return with up to 70.6% fewer samples, and outperforms GB-PL baselines by at least 54% in closed-loop return.
Baha Zarrouki, Arslan Thobani, Jasper Hoffmann et al.· 0 citations
A lightweight offline-primitive-based dataset collection framework is designed to produce safe and high-quality trajectory primitives in non-convex environments and demonstrates superiority in both planning latency and target-reaching progress quality.
Zhi-Tao Liu, Guang-Tong Xu, Zi-Han Wang et al.· IEEE Transactions on Industr...· 0 citations
DronesCATS-Agent, an architecture where the MLLM is a swappable component, and DroneCATS, a benchmark treating the model as the independent variable are introduced, an architecture where the MLLM is a swappable component and DroneCATS, a benchmark treating the model as the independent variable.
Jaewoo Park, Minyoung Lee, Sukmin Seo et al.· 0 citations
The Rao-Blackwellized online POMDP (RB-POMDP) framework is extended to improve its generalizability in high-dimensional settings through hybrid continuous-discrete belief representations and suggests that structured high-dimensional robotic problems admitting tractable sufficient statistics can be effectively leveraged...
Jiho Lee, Nisar R. Ahmed, Kyle Hollins Wray et al.· 0 citations
EmbodiedSkills is a unified framework that treats each skill decision as an execution proposal: the runtime checks its prerequisites before execution and verifies the outcome afterward and provides a trainable and inspectable agent layer for turning low-level VLA policies into closed-loop embodied systems.
Wei Wang, Wen-Qiao Zhang, Yu-Tong Lin et al.· 3 citations
Adding an auxiliary supervised contrastive (InfoNCE) loss during world-model warmup substantially improves sleep-phase clustering, and adding an auxiliary supervised contrastive loss during world-model warmup substantially improves sleep-phase clustering on LIBERO.
A Dual Neural-Calibrated Interacting Multiple Model (DNC-IMM) that improves adaptability to driving context while preserving the probabilistic structure and interpretability of a conventional IMM is proposed.
Woong-Chan Byun, Seung-Hyun Kong· The Journal of The Korea Ins...· 0 citations
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.
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
