A diffusion-based multi-robot planner trained on few agents generalizes to larger numbers during deployment using inter-agent attention and temporal convolution.
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Contact-Grounded Policy predicts coupled robot-state and tactile trajectories with a diffusion model and maps them via a learned consistency function to executable targets for compliance controllers, outperforming standard visuotactile diffusion baselines on physical and simulated dexterous tasks.
Gaussian Process regression supplies a data-driven feed-forward term whose fidelity measure is used to lower feedback gains in high-confidence regions for soft-robot tracking control.
OpenPRC provides a schema-driven framework with five modules for GPU physics simulation, experimental vision ingestion, reservoir learning, information analysis, and physics-aware optimization to enable consistent PRC evaluation from simulations and real experiments.
MOBIUS is a multi-modal bipedal robot with hybrid reinforcement learning and force control plus an MIQCP planner that enables walking, crawling, climbing, and rolling on varied terrains.
Compute and motion are tightly intertwined in MAVs, requiring cyber-physical co-design for optimal mission metrics, as shown via analytical models, simulation, end-to-end benchmarking, and the open-sourced MAVBench tool suite.
An OctoMap frontier exploration method achieves O(number of frontiers) complexity via sensor modeling and Bayesian information gain estimation, delivering up to 54% faster exploration than standard baselines.