Search Results for author:
Found 10 papers, 3 papers with code
Humanoid Locomotion as Next Token Prediction
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language.
Prompt a Robot to Walk with Large Language Models
Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains.
Real-World Humanoid Locomotion with Reinforcement Learning
Humanoid robots that can autonomously operate in diverse environments have the potential to help address labour shortages in factories, assist elderly at homes, and colonize new planets.
Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions
This feasibility analysis results in a set of richness conditions that the available information about the system should satisfy to guarantee that a safe control action can be found at all times.
Learning Torque Control for Quadrupedal Locomotion
Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots.
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
By embedding the optimization procedure of the exponential control barrier function based quadratic program (ECBF-QP) as a differentiable layer within a deep learning architecture, we propose a differentiable safety-critical control framework that enables generalization to new environments for high relative-degree systems with forward invariance guarantees.
Pointwise Feasibility of Gaussian Process-based Safety-Critical Control under Model Uncertainty
However, since these constraints rely on a model of the system, when this model is inaccurate the guarantees of safety and stability can be easily lost.
Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change
This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment.
Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics
This paper presents a method to design a min-norm Control Lyapunov Function (CLF)-based stabilizing controller for a control-affine system with uncertain dynamics using Gaussian Process (GP) regression.
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control.
