Search Results for author: Tingnan Zhang

Found 11 papers, 2 papers with code

Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions

no code implementations28 Mar 2022 Alejandro Escontrela, Xue Bin Peng, Wenhao Yu, Tingnan Zhang, Atil Iscen, Ken Goldberg, Pieter Abbeel

We also demonstrate that an effective style reward can be learned from a few seconds of motion capture data gathered from a German Shepherd and leads to energy-efficient locomotion strategies with natural gait transitions.

Safe Reinforcement Learning for Legged Locomotion

no code implementations5 Mar 2022 Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, Wenhao Yu

In this paper, we propose a safe reinforcement learning framework that switches between a safe recovery policy that prevents the robot from entering unsafe states, and a learner policy that is optimized to complete the task.

reinforcement-learning Safe Reinforcement Learning

Fast and Efficient Locomotion via Learned Gait Transitions

1 code implementation9 Apr 2021 Yuxiang Yang, Tingnan Zhang, Erwin Coumans, Jie Tan, Byron Boots

We focus on the problem of developing energy efficient controllers for quadrupedal robots.

On the Sample Complexity of Stability Constrained Imitation Learning

no code implementations18 Feb 2021 Stephen Tu, Alexander Robey, Tingnan Zhang, Nikolai Matni

We study the following question in the context of imitation learning for continuous control: how are the underlying stability properties of an expert policy reflected in the sample-complexity of an imitation learning task?

Continuous Control Generalization Bounds +1

Learning Agile Robotic Locomotion Skills by Imitating Animals

no code implementations2 Apr 2020 Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, Sergey Levine

In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.

Domain Adaptation Imitation Learning +1

Policies Modulating Trajectory Generators

2 code implementations7 Oct 2019 Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.

Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation

no code implementations27 Sep 2019 Xinlei Pan, Tingnan Zhang, Brian Ichter, Aleksandra Faust, Jie Tan, Sehoon Ha

Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on legged robots from human (third-person perspective) demonstrations, enabling high-quality navigation and cost-effective data collection.

Disentanglement Imitation Learning +2

Data Efficient Reinforcement Learning for Legged Robots

no code implementations8 Jul 2019 Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani

We present a model-based framework for robot locomotion that achieves walking based on only 4. 5 minutes (45, 000 control steps) of data collected on a quadruped robot.

Legged Robots reinforcement-learning +1

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