Search Results for author: Ken Caluwaerts

Found 7 papers, 2 papers with code

From Pixels to Legs: Hierarchical Learning of Quadruped Locomotion

no code implementations23 Nov 2020 Deepali Jain, Atil Iscen, Ken Caluwaerts

We show that hierarchical policies can concurrently learn to locomote and navigate in these environments, and show they are more efficient than non-hierarchical neural network policies.

Hierarchical Reinforcement Learning Legged Robots

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

no code implementations2 Mar 2020 Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan

Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world.

Legged Robots Meta-Learning

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.

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 Safe Exploration

Hierarchical Reinforcement Learning for Quadruped Locomotion

no code implementations22 May 2019 Deepali Jain, Atil Iscen, Ken Caluwaerts

We test our framework on a path-following task for a dynamic quadruped robot and we show that steering behaviors automatically emerge in the latent command space as low-level skills are needed for this task.

Hierarchical Reinforcement Learning

NoRML: No-Reward Meta Learning

1 code implementation4 Mar 2019 Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn

To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).


Deep Reinforcement Learning for Tensegrity Robot Locomotion

no code implementations28 Sep 2016 Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine

We evaluate our method with real-world and simulated experiments on the SUPERball tensegrity robot, showing that the learned policies generalize to changes in system parameters, unreliable sensor measurements, and variation in environmental conditions, including varied terrains and a range of different gravities.

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