Akshara Rai

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Learning Navigation Skills for Legged Robots with Learned Robot Embeddings

Navigation policies are commonly learned on idealized cylinder agents in...
11/24/2020 ∙ by Joanne Truong, et al. ∙ 0 ∙ share

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Leveraging Forward Model Prediction Error for Learning Control

Learning for model based control can be sample-efficient and generalize ...
11/07/2020 ∙ by Sarah Bechtle, et al. ∙ 0 ∙ share

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Model-Based Inverse Reinforcement Learning from Visual Demonstrations

Scaling model-based inverse reinforcement learning (IRL) to real robotic...
10/18/2020 ∙ by Neha Das, et al. ∙ 0 ∙ share

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Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion

Hierarchical learning has been successful at learning generalizable loco...
08/27/2020 ∙ by Tianyu Li, et al. ∙ 0 ∙ share

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Learning State-Dependent Losses for Inverse Dynamics Learning

Being able to quickly adapt to changes in dynamics is paramount in model...
03/10/2020 ∙ by Kristen Morse, et al. ∙ 0 ∙ share

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Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Bayesian optimization (BO) is a popular approach to optimize expensive-t...
01/31/2020 ∙ by Benjamin Letham, et al. ∙ 16 ∙ share

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Encoding Physical Constraints in Differentiable Newton-Euler Algorithm

The recursive Newton-Euler Algorithm (RNEA) is a popular technique in ro...
01/24/2020 ∙ by Giovanni Sutanto, et al. ∙ 0 ∙ share

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Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning

Learning to locomote to arbitrary goals on hardware remains a challengin...
09/26/2019 ∙ by Tianyu Li, et al. ∙ 0 ∙ share

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Bayesian Optimization in Variational Latent Spaces with Dynamic Compression

Data-efficiency is crucial for autonomous robots to adapt to new tasks a...
07/10/2019 ∙ by Rika Antonova, et al. ∙ 0 ∙ share

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Curious iLQR: Resolving Uncertainty in Model-based RL

Curiosity as a means to explore during reinforcement learning problems h...
04/15/2019 ∙ by Sarah Bechtle, et al. ∙ 0 ∙ share

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Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped

Learning controllers for bipedal robots is a challenging problem, often ...
09/28/2018 ∙ by Tianyu Li, et al. ∙ 0 ∙ share

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Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots

Learning for control can acquire controllers for novel robotic tasks, pa...
05/07/2018 ∙ by Akshara Rai, et al. ∙ 0 ∙ share

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Akshara Rai

Featured Co-authors

Dhruv Batra

Danica Kragic

Yuandong Tian

Sonia Chernova

Roberto Calandra

Ludovic Righetti

Deepak Pathak

Franziska Meier

Dinesh Jayaraman

Tianyu Li

Denis Yarats

Learning Navigation Skills for Legged Robots with Learned Robot Embeddings

Leveraging Forward Model Prediction Error for Learning Control

Model-Based Inverse Reinforcement Learning from Visual Demonstrations

Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion

Learning State-Dependent Losses for Inverse Dynamics Learning

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization

Encoding Physical Constraints in Differentiable Newton-Euler Algorithm

Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning

Bayesian Optimization in Variational Latent Spaces with Dynamic Compression

Curious iLQR: Resolving Uncertainty in Model-based RL

Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped

Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots