Search Results for author: Soroush Nasiriany

Found 13 papers, 5 papers with code

RT-Affordance: Affordances are Versatile Intermediate Representations for Robot Manipulation

no code implementations5 Nov 2024 Soroush Nasiriany, Sean Kirmani, Tianli Ding, Laura Smith, Yuke Zhu, Danny Driess, Dorsa Sadigh, Ted Xiao

Our method, RT-Affordance, is a hierarchical model that first proposes an affordance plan given the task language, and then conditions the policy on this affordance plan to perform manipulation.

Robot Manipulation

RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

no code implementations4 Jun 2024 Soroush Nasiriany, Abhiram Maddukuri, Lance Zhang, Adeet Parikh, Aaron Lo, Abhishek Joshi, Ajay Mandlekar, Yuke Zhu

We advocate using realistic physical simulation as a means to scale environments, tasks, and datasets for robot learning methods.

Imitation Learning Text to 3D

MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations

2 code implementations26 Oct 2023 Ajay Mandlekar, Soroush Nasiriany, Bowen Wen, Iretiayo Akinola, Yashraj Narang, Linxi Fan, Yuke Zhu, Dieter Fox

Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents.

Imitation Learning

Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment

no code implementations15 Nov 2022 Huihan Liu, Soroush Nasiriany, Lance Zhang, Zhiyao Bao, Yuke Zhu

To harness the capabilities of state-of-the-art robot learning models while embracing their imperfections, we present Sirius, a principled framework for humans and robots to collaborate through a division of work.

Decision Making

Learning and Retrieval from Prior Data for Skill-based Imitation Learning

no code implementations20 Oct 2022 Soroush Nasiriany, Tian Gao, Ajay Mandlekar, Yuke Zhu

Imitation learning offers a promising path for robots to learn general-purpose behaviors, but traditionally has exhibited limited scalability due to high data supervision requirements and brittle generalization.

Data Augmentation Imitation Learning +2

Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks

1 code implementation7 Oct 2021 Soroush Nasiriany, Huihan Liu, Yuke Zhu

Realistic manipulation tasks require a robot to interact with an environment with a prolonged sequence of motor actions.

Deep Reinforcement Learning reinforcement-learning +1

What Matters in Learning from Offline Human Demonstrations for Robot Manipulation

1 code implementation6 Aug 2021 Ajay Mandlekar, Danfei Xu, Josiah Wong, Soroush Nasiriany, Chen Wang, Rohun Kulkarni, Li Fei-Fei, Silvio Savarese, Yuke Zhu, Roberto Martín-Martín

Based on the study, we derive a series of lessons including the sensitivity to different algorithmic design choices, the dependence on the quality of the demonstrations, and the variability based on the stopping criteria due to the different objectives in training and evaluation.

Imitation Learning reinforcement-learning +3

DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies

no code implementations23 Apr 2021 Soroush Nasiriany, Vitchyr H. Pong, Ashvin Nair, Alexander Khazatsky, Glen Berseth, Sergey Levine

Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity.

reinforcement-learning Reinforcement Learning +2

Planning with Goal-Conditioned Policies

1 code implementation NeurIPS 2019 Soroush Nasiriany, Vitchyr H. Pong, Steven Lin, Sergey Levine

Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors.

Decision Making reinforcement-learning +5

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