As robots become increasingly integrated into various industries, understanding how humans respond to robotic failures is critical. This study systematically examines trust dynamics and system design by analyzing human reactions to robot failures. We conducted a four-stage survey to explore how trust evolves throughout...
Manual and teleoperated intraocular instrument motion was compared with the trocar constraint, the instrument, the eye model and the tracking source common to both conditions, so that the control interface was the only factor varied.
Korab Hoxha, M. Imamović, Angelo Henriques et al.· 0 citations
The Configuration-Aware Limit Model (CALM) is introduced, which translates stopping-distance distributions into configuration-dependent population-coverage boundaries and support treating body configuration as a planning variable while distinguishing physical safety, behavioral intervention, and subjective cost.
Ghost-in-the-Loop, a teleoperation framework that supports both human-operated and AI-generated control of a robot's voice, facial expressions, and gestures while maintaining a consistent embodiment, provides initial insights into agency perception in embodied human--AI communication.
Xiang Li, Koya Dendo, Keigo Minamida et al.· 0 citations
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Preference-based reinforcement learning (PbRL) enables agents to learn control policies without requiring manually designed reward functions, making it well-suited for tasks where objectives are difficult to formalize or inherently subjective. Acrobatic flight poses a particularly challenging problem due to its complex...
Colin Merk, Ismail Geles, Jiaxu Xing et al.· 0 citations
A two-stage controllable tactile image generation framework that generates realistic tactile images conditioned on a single reference tactile image, contact force, and contact pose by grounding generation in these important physical priors is proposed.
Dong-Yu Luo, Ke-Lin Yu, A. Shahidzadeh et al.· 0 citations
Three properties determine whether a differentiable simulator can drive gradient-based optimization through contact: simulation accuracy, gradient reliability, and per-iteration cost. Tape-based engines such as MJX and Newton Semi-Implicit require timesteps small enough to keep contacts numerically tractable, and their...
This work introduces a reinforcement learning approach that forgoes predefined plans entirely, instead generating construction sequences adaptively as the structure is built, and evaluates the algorithm, HSAC, against the prior method hybrid-PPO (HPPO), demonstrating significantly higher asymptotic performance and good...
Gabriel Vallat, Maryam Kamgarpour, S. Parascho· 0 citations
In world model planning, sensing inputs pass through an encoder and predictor before affecting planner decisions, so final task success alone cannot reveal where sensing disturbances attenuate or persist in the pipeline. We apply 10 visual and temporal sensing degradations to a world model planner and track their effec...
This paper introduces a framework based on spatially conditioned Multi-Agent Transformers to efficiently learn robust control policies for a DDM system grounded in an array of 64 soft delta robots arranged in an 8x8 grid and proposes an action selection formulation to analyze the trade-off between task performance and...
Reasoning over language instructions in embodied tasks such as robotics often requires understanding spatial relations from a speaker's situated perspective. Humans infer such perspectives from shared environmental knowledge, activity context, and commonsense. Recent vision-language models (VLMs) appear capable of spat...
Mimo Shirasaka, Haochen Zhang, Yonatan Bisk· 0 citations
CAVEAT, a diffusion policy conditioned on a recurrent internal state updated from fused LiDAR, visual, and pose features and trained from trajectories generated by the map-based FUELv2 expert, is investigated through simulation.
Steven Visch, Nicolò Botteghi, Antonio Franchi et al.· 0 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.
