no code implementations • 29 Feb 2024 • Ilija Radosavovic, Bike Zhang, Baifeng Shi, Jathushan Rajasegaran, Sarthak Kamat, Trevor Darrell, Koushil Sreenath, Jitendra Malik
We cast real-world humanoid control as a next token prediction problem, akin to predicting the next word in language.
1 code implementation • 18 Sep 2023 • Yen-Jen Wang, Bike Zhang, Jianyu Chen, Koushil Sreenath
Large language models (LLMs) pre-trained on vast internet-scale data have showcased remarkable capabilities across diverse domains.
no code implementations • 6 Mar 2023 • Ilija Radosavovic, Tete Xiao, Bike Zhang, Trevor Darrell, Jitendra Malik, Koushil Sreenath
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.
no code implementations • 23 Aug 2022 • Fernando Castañeda, Jason J. Choi, Wonsuhk Jung, Bike Zhang, Claire J. Tomlin, Koushil Sreenath
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.
no code implementations • 10 Mar 2022 • Shuxiao Chen, Bike Zhang, Mark W. Mueller, Akshara Rai, Koushil Sreenath
Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots.
no code implementations • 4 Jan 2022 • Hengbo Ma, Bike Zhang, Masayoshi Tomizuka, Koushil Sreenath
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.
no code implementations • 13 Jun 2021 • Fernando Castañeda, Jason J. Choi, Bike Zhang, Claire J. Tomlin, Koushil Sreenath
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.
1 code implementation • 23 Mar 2021 • Suiyi He, Jun Zeng, Bike Zhang, Koushil Sreenath
This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment.
no code implementations • 14 Nov 2020 • Fernando Castañeda, Jason J. Choi, Bike Zhang, Claire J. Tomlin, Koushil Sreenath
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.
2 code implementations • 22 Jul 2020 • Jun Zeng, Bike Zhang, Koushil Sreenath
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.