NeHMO approximates a safety value function via neural Hamilton-Jacobi reachability learning and integrates it into decentralized trajectory optimization for scalable safe multi-arm robotic motion planning.
On the implementation of an interior- point filter line-search algorithm for large-scale nonlinear program- ming
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A two-layer system uses multi-rate NMPC to jointly plan contact points and body trajectories for wall-supported bipedal walking in quadrupeds, showing 2.9 times higher simulation success than heuristic MPC on rough terrain.
Backward time-reversed conflict-based search finds initial collision-free simultaneous-arrival trajectories for agents at rest, which are then refined via distributed nonlinear ADMM optimal control.
GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.
A CBF-augmented NMPC framework for two quadrupeds models the robot-payload system as a DAE and enforces collision avoidance in hardware tests under uncertainty.
A hybrid search-plus-optimal-control framework that produces optimized, kinematically feasible trajectories for multiple agents by warm-starting an OCP from an initial feasible solution.
citing papers explorer
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NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Arm Motion Planning
NeHMO approximates a safety value function via neural Hamilton-Jacobi reachability learning and integrates it into decentralized trajectory optimization for scalable safe multi-arm robotic motion planning.
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Multi-Rate Nonlinear Model Predictive Control for Wall-Supported Bipedal Locomotion of Quadrupedal Robots
A two-layer system uses multi-rate NMPC to jointly plan contact points and body trajectories for wall-supported bipedal walking in quadrupeds, showing 2.9 times higher simulation success than heuristic MPC on rough terrain.
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Multi-Agent Motion Planning for Simultaneous Arrival using Time-Reversed Search and Distributed Optimal Control
Backward time-reversed conflict-based search finds initial collision-free simultaneous-arrival trajectories for agents at rest, which are then refined via distributed nonlinear ADMM optimal control.
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GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.
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Safety-Critical Centralized Nonlinear MPC for Cooperative Payload Transportation by Two Quadrupedal Robots
A CBF-augmented NMPC framework for two quadrupeds models the robot-payload system as a DAE and enforces collision avoidance in hardware tests under uncertainty.
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Optimized and kinematically feasible multi-agent motion planning
A hybrid search-plus-optimal-control framework that produces optimized, kinematically feasible trajectories for multiple agents by warm-starting an OCP from an initial feasible solution.