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.
CasADi – A software framework for nonlinear optimization and optimal control
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Decoupled strategic RL policy and expert-informed execution module for autonomous endovascular navigation, reporting >96% success rate, 29.3% fewer steps, and 13% less trajectory variance in simulation and real robot tests.
An adversarial test reveals that only three of fourteen transcription methods for 6-DOF rocket landing optimization meet validation criteria, with implicit GL2 matching or exceeding explicit RK6 in fidelity and practical performance.
Jointly calibrating noise covariances and leg kinematics with a bi-level, estimator-in-the-loop optimization improves legged-robot state estimation accuracy.
Terrain-consistent reference modulation during RL training yields SE(2)-controllable humanoid locomotion policies that improve tracking in simulation and enable over 70 m closed-loop autonomous navigation on rough terrain and stairs on the Unitree G1 with onboard computation.
A hybrid discrete-event systems supervisor enables safe recovery of lost drones in simulated swarms across four scenarios, with an additional supervisor for regrouping.
A novel diffusion variant accelerates minimum-time planning for redundant dual-arm robots by replacing gradient-based solving of the nonconvex high-level problem with probabilistic sampling, yielding 35x faster runtime and 34% less path error.
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.
citing papers explorer
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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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Learning Expert Strategy for Autonomous Robotic Endovascular Intervention via Decoupled Procedural Execution
Decoupled strategic RL policy and expert-informed execution module for autonomous endovascular navigation, reporting >96% success rate, 29.3% fewer steps, and 13% less trajectory variance in simulation and real robot tests.
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Transcription-Induced Failure Modes in 6-DOF Rocket Landing Trajectory Optimization
An adversarial test reveals that only three of fourteen transcription methods for 6-DOF rocket landing optimization meet validation criteria, with implicit GL2 matching or exceeding explicit RK6 in fidelity and practical performance.
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Simultaneous Calibration of Noise Covariance and Kinematics for State Estimation of Legged Robots via Bi-level Optimization
Jointly calibrating noise covariances and leg kinematics with a bi-level, estimator-in-the-loop optimization improves legged-robot state estimation accuracy.
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Terrain Consistent Reference-Guided RL for Humanoid Navigation Autonomy
Terrain-consistent reference modulation during RL training yields SE(2)-controllable humanoid locomotion policies that improve tracking in simulation and enable over 70 m closed-loop autonomous navigation on rough terrain and stairs on the Unitree G1 with onboard computation.
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A Case Study in Recovery of Drones using Discrete-Event Systems
A hybrid discrete-event systems supervisor enables safe recovery of lost drones in simulated swarms across four scenarios, with an additional supervisor for regrouping.
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Diffusion-Based Optimization for Accelerated Convergence of Redundant Dual-Arm Minimum Time Problems
A novel diffusion variant accelerates minimum-time planning for redundant dual-arm robots by replacing gradient-based solving of the nonconvex high-level problem with probabilistic sampling, yielding 35x faster runtime and 34% less path error.
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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.