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Tyler Westenbroek

9 accepted papers

2026

Emergent Dexterity Via Diverse Resets and Large-Scale Reinforcement Learning

Patrick Yin, Tyler Westenbroek, Zhengyu Zhang, Ignacio Dagnino, Eeshani Shilamkar, Numfor Mbiziwo-Tiapo +5
ICLR 2026poster

Reinforcement learning in GPU-enabled physics simulation has been the driving force behind many of the breakthroughs in sim-to-real robot learning. However, current approaches for data generation in simulation are unwieldy and task-specific, requiring extensive human effort to engineer training curr…

Cited by 0Source ↗Scholar ↗
2026

RFS: Reinforcement learning with Residual flow steering for dexterous manipulation

Entong Su, Tyler Westenbroek, Anusha Nagabandi, Abhishek Gupta
ICLR 2026poster

Imitation learning has been an effective tool for bootstrapping sequential decision making behavior, showing surprisingly strong results as methods are scaled up to high-dimensional, dexterous problems in robotics. These ``behavior cloning" methods have been further bolstered by the integration of g…

Cited by 0Source ↗code ↗Scholar ↗
2026

Simulation Distillation: Pretraining World Models in Simulation for Rapid Real-World Adaptation

Jacob Levy, Tyler Westenbroek, Kevin Huang, Fernando Palafox, Patrick Yin, Shayegan Omidshafiei +3
RSS 2026poster

Simulation-to-real transfer remains a central challenge in robotics, as mismatches between simulated and real-world dynamics often lead to failures. While reinforcement learning offers a principled mechanism for adaptation, existing sim-to-real finetuning methods struggle with exploration and long-h…

Cited by 0Source ↗Scholar ↗
2025

Rapidly Adapting Policies to the Real-World via Simulation-Guided Fine-Tuning

Patrick Yin, Tyler Westenbroek, Ching-An Cheng, Andrey Kolobov, Abhishek Gupta
ICLR 2025poster

Robot learning requires a considerable amount of high-quality data to realize the promise of generalization. However, large data sets are costly to collect in the real world. Physics simulators can cheaply generate vast data sets with broad coverage over states, actions, and environments. However, p…

Cited by 2Source ↗PDF ↗Scholar ↗
2024

Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models

Jacob Levy, Tyler Westenbroek, David Fridovich-Keil
CoRL 2024poster

Traditionally, model-based reinforcement learning (MBRL) methods exploit neural networks as flexible function approximators to represent $\textit{a priori}$ unknown environment dynamics. However, training data are typically scarce in practice, and these black-box models often fail to generalize. Mod…

Cited by 6Source ↗code ↗Scholar ↗
2023

Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models

Tyler Westenbroek, Jacob Levy, David Fridovich-Keil
CoRL 2023poster

We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data. In recent years, policy gradient methods have emerged as a promising paradigm for training control policies in simulation. However, these approaches often remain too data inefficie…

Cited by 3Source ↗code ↗Scholar ↗
2023

The Power of Learned Locally Linear Models for Nonlinear Policy Optimization

Daniel Pfrommer, Max Simchowitz, Tyler Westenbroek, Nikolai Matni, Stephen Tu
ICML 2023poster

A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e.g. $\mathtt{iLQR}$ - on the learned model to minimize a target cost. This paper conducts a rigorous analysis of a simplified variant of this strategy f…

Cited by 4Source ↗PDF ↗Scholar ↗
2022

Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning

Tyler Westenbroek, Fernando Castaneda, Ayush Agrawal, Shankar Sastry, Koushil Sreenath
CoRL 2022poster

Recent advances in the reinforcement learning (RL) literature have enabled roboticists to automatically train complex policies in simulated environments. However, due to the poor sample complexity of these methods, solving RL problems using real-world data remains a challenging problem. This paper i…

Cited by 28Source ↗Scholar ↗
2020

Feedback Linearization for Uncertain Systems via Reinforcement Learning

Tyler Westenbroek, David Fridovich-Keil, Eric Mazumdar, Shreyas Arora, Valmik Prabhu, S. Shankar Sastry +1
ICRA 2020poster

We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics. Feedback linearization is a technique from nonlinear control which renders the input-output dyna…

Cited by 49Source ↗Scholar ↗