Dushyant Rao — accepted AI-conference papers

2025

DemoStart: Demonstration-Led Auto-Curriculum Applied to Sim-to-Real with Multi-Fingered Robots

Maria Bauzá, Jose Enriaue Chen, Valentin Dalibard, Nimrod Gileadi, Roland Hafner, Murilo F. Martins +10

ICRA 2025

We present DemoStart, a novel auto-curriculum reinforcement learning method capable of learning complex manipulation behaviors on an arm equipped with a three- fingered robotic hand, from only a sparse reward and a handful of demonstrations in simulation. Learning from simulation drastically reduces

Cited by 12 Source ↗Scholar ↗

2025

Exploiting Policy Idling for Dexterous Manipulation

Annie S. Chen, Philemon Brakel, Antonia Bronars, Annie Xie, Sandy Han Huang, Oliver Groth +4

IROS 2025

Learning based methods for dexterous manipulation have made notable progress in recent years, and they can now produce solutions to complex tasks. However, learned policies often still lack reliability and exhibit limited robustness to important factors of variation. One failure pattern that can be

Cited by 1 Source ↗Scholar ↗

2024

Learning to Learn Faster from Human Feedback with Language Model Predictive Control

Jacky Liang, Fei Xia, Wenhao Yu, Andy Zeng, Maria Attarian, Maria Bauza Villalonga +43

RSS 2024poster

Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new tasks. However, these capabilities (driven by in-co…

Cited by 35 Source ↗PDF ↗code ↗Scholar ↗

2022

Learning transferable motor skills with hierarchical latent mixture policies

Dushyant Rao, Fereshteh Sadeghi, Leonard Hasenclever, Markus Wulfmeier, Martina Zambelli, Giulia Vezzani +5

ICLR 2022spotlight

For robots operating in the real world, it is desirable to learn reusable abstract behaviours that can effectively be transferred across numerous tasks and scenarios. We propose an approach to learn skills from data using a hierarchical mixture latent variable model. Our method exploits a multi-leve…

Cited by 38 Source ↗PDF ↗Scholar ↗

2021

Data-efficient Hindsight Off-policy Option Learning

Markus Wulfmeier, Dushyant Rao, Roland Hafner, Thomas Lampe, Abbas Abdolmaleki, Tim Hertweck +5

ICML 2021spotlight

We introduce Hindsight Off-policy Options (HO2), a data-efficient option learning algorithm. Given any trajectory, HO2 infers likely option choices and backpropagates through the dynamic programming inference procedure to robustly train all policy components off-policy and end-to-end. The approach o…

Cited by 52 Source ↗PDF ↗Scholar ↗

2020

Attention-Privileged Reinforcement Learning

Sasha Salter, Dushyant Rao, Markus Wulfmeier, Raia Hadsell, Ingmar Posner

CoRL 2020

Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observation space). Visual domain randomisation encourages transfer by training over visual factors of variation that may be enc

Cited by 0 Source ↗PDF ↗Scholar ↗

2019

Continual Unsupervised Representation Learning

Dushyant Rao, Francesco Visin, Andrei Rusu, Razvan Pascanu, Yee Whye Teh, Raia Hadsell

NeurIPS 2019poster

Continual learning aims to improve the ability of modern learning systems to deal with non-stationary distributions, typically by attempting to learn a series of tasks sequentially. Prior art in the field has largely considered supervised or reinforcement learning tasks, and often assumes full knowl…

Cited by 341 Source ↗PDF ↗code ↗Scholar ↗

2019

Meta-Learning with Latent Embedding Optimization

Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero +1

ICLR 2019poster

Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possi…

Cited by 1771 Source ↗PDF ↗code ↗Scholar ↗

2018

Resource-Performance Tradeoff Analysis for Mobile Robots

Morteza Lahijanian, María Svorenová, Akshay A. Morye, Brian Yeomans, Dushyant Rao, Ingmar Posner +3

RA-L 2018

The design of mobile autonomous robots is challenging due to the limited on-board resources such as processing power and energy. A promising approach is to generate intelligent schedules that reduce the resource consumption while maintaining best performance, or more interestingly, to tradeoff reduc

Cited by 33 Source ↗Scholar ↗

2017

Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks

Martin Engelcke, Dushyant Rao, Dominic Zeng Wang, Chi Hay Tong, Ingmar Posner

ICRA 2017poster

This paper proposes a computationally efficient approach to detecting objects natively in 3D point clouds using convolutional neural networks (CNNs). In particular, this is achieved by leveraging a feature-centric voting scheme to implement novel convolutional layers which explicitly exploit the spa…

Cited by 719 Source ↗Scholar ↗

2016

Multimodal information-theoretic measures for autonomous exploration

Dushyant Rao, Asher Bender, Stefan B. Williams, Oscar Pizarro

ICRA 2016

Autonomous underwater vehicles (AUVs) are widely used to perform information gathering missions in unseen environments. Given the sheer size of the ocean environment, and the time and energy constraints of an AUV, it is important to consider the potential utility of candidate missions when performin

Cited by 5 Source ↗Scholar ↗