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Tsung-Yen Yang

11 accepted papers

2025

PARTNR: A Benchmark for Planning and Reasoning in Embodied Multi-agent Tasks

Matthew Chang, Gunjan Chhablani, Alexander Clegg, Mikael Dallaire Cote, Ruta Desai, Michal Hlavac +14
ICLR 2025poster

We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhibit characteristics of everyday tasks, such as spatial, temporal, and heterogeneous agent capability constraints. We empl…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2024

ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation

Naoki Yokoyama, Alexander Clegg, Joanne Truong, Eric Undersander, Tsung-Yen Yang, Sergio Arnaud +3
RA-L 2024

We present Adaptive Skill Coordination (ASC) – an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating to another location, and placing it). ASC consists of three components – (1) a library of basic visuomotor <italic xmlns:m

Cited by 74Source ↗Scholar ↗
2024

Habitat 3.0: A Co-Habitat for Humans, Avatars, and Robots

Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Tsung-Yen Yang, Ruslan Partsey +17
ICLR 2024poster

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accurate humanoid simulation: addressing challenges in modeling complex deformable bodies and diversity in appearance and mot…

Cited by 111Source ↗PDF ↗Scholar ↗
2024

Situated Instruction Following

So Yeon Min*, Xavier Puig, Devendra Singh Chaplot, Tsung-Yen Yang, Priyam Parashar, Akshara Rai +3
ECCV 2024poster

"Language is never spoken in a vacuum. It is expressed, comprehended, and contextualized within the holistic backdrop of the speaker’s history, actions, and environment. Since humans are used to communicating efficiently with situated language, the practicality of robotic assistants hinge on their a…

Cited by 4Source ↗PDF ↗code ↗Scholar ↗
2023

HomeRobot: Open-Vocabulary Mobile Manipulation

Sriram Yenamandra, Arun Ramachandran, Karmesh Yadav, Austin S Wang, Mukul Khanna, Theophile Gervet +12
CoRL 2023poster

HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks. Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This i…

Cited by 98Source ↗code ↗Scholar ↗
2022

Learning Physics Constrained Dynamics Using Autoencoders

Tsung-Yen Yang, Justinian P. Rosca, Karthik R Narasimhan, Peter Ramadge
NeurIPS 2022accept

We consider the problem of estimating states (e.g., position and velocity) and physical parameters (e.g., friction, elasticity) from a sequence of observations when provided a dynamic equation that describes the behavior of the system. The dynamic equation can arise from first principles (e.g., Newt…

Cited by 26Source ↗PDF ↗Scholar ↗
2022

Leveraging Language for Accelerated Learning of Tool Manipulation

Allen Z. Ren, Bharat Govil, Tsung-Yen Yang, Karthik R Narasimhan, Anirudha Majumdar
CoRL 2022poster

Robust and generalized tool manipulation requires an understanding of the properties and affordances of different tools. We investigate whether linguistic information about a tool (e.g., its geometry, common uses) can help control policies adapt faster to new tools for a given task. We obtain divers…

Cited by 53Source ↗Scholar ↗
2022

Safe Reinforcement Learning for Legged Locomotion

Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, Wenhao Yu
IROS 2022poster

Designing control policies for legged locomotion11In this work, we specifically consider quadruped locomotion. is complex due to the under-actuated and non-continuous robot dynamics. Model-free reinforcement learning provides promising tools to tackle this challenge. However, a major bottleneck of a…

Cited by 42Source ↗Scholar ↗
2021

Accelerating Safe Reinforcement Learning with Constraint-mismatched Baseline Policies

Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J Ramadge
ICML 2021spotlight

We consider the problem of reinforcement learning when provided with (1) a baseline control policy and (2) a set of constraints that the learner must satisfy. The baseline policy can arise from demonstration data or a teacher agent and may provide useful cues for learning, but it might also be sub-o…

Cited by 26Source ↗PDF ↗Scholar ↗
2021

Safe Reinforcement Learning with Natural Language Constraints

Tsung-Yen Yang, Michael Hu, Yinlam Chow, Peter Ramadge, Karthik R Narasimhan
NeurIPS 2021spotlight

While safe reinforcement learning (RL) holds great promise for many practical applications like robotics or autonomous cars, current approaches require specifying constraints in mathematical form. Such specifications demand domain expertise, limiting the adoption of safe RL. In this paper, we propos…

Cited by 41Source ↗PDF ↗Scholar ↗
2020

Projection-Based Constrained Policy Optimization

Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge
ICLR 2020poster

We consider the problem of learning control policies that optimize a reward function while satisfying constraints due to considerations of safety, fairness, or other costs. We propose a new algorithm - Projection-Based Constrained Policy Optimization (PCPO), an iterative method for optimizing polici…

Cited by 307Source ↗Scholar ↗