Khimya Khetarpal

Byron Boots

Siddhartha Srinivasa

A robot navigating an outdoor environment with no prior knowledge of the space must rely on its local sensing, which is in the form of a loc… (voir plus)al metric map or local policy with some fixed horizon. A limited planning horizon can often result in myopic decisions leading the robot off course or worse, into very difficult terrain. In this work, we make a key observation that long range navigation only necessitates identifying good frontier directions for planning instead of full map knowledge. To address this, we introduce Long Range Navigator (LRN), which learns to predict affordable’ frontier directions from high-dimensional camera images. LRN is trained entirely on unlabeled egocentric videos, making it scalable and adaptable. In off-road tests on Spot and a large vehicle, LRN reduces human interventions and improves decision speed when integrated into existing navigation stacks.

2025-10-07

Proceedings of The 8th Conference on Robot Learning (publié)

proceedings.mlr.press

Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps

Matt Schmittle

Rohan Baijal

Nathan Hatch

Rosario Scalise

Mateo Guaman Castro

Sidharth Talia

Byron Boots

Siddhartha Srinivasa

2025-08-08

robot-learning.org/CoRL/2025/Conference (poster)

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning

Adriana Hugessen

Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (voir plus)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,

2025-07-01

rl-conference.cc/RLC/2025/Workshop/RLBrew (publié)

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning

Adriana Hugessen

Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (voir plus)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,

2025-06-11

ArXiv (prépublication)

Self-Predictive Representations for Combinatorial Generalization in Behavioral Cloning

Adriana Hugessen

Behavioral cloning (BC) methods trained with supervised learning (SL) are an effective way to learn policies from human demonstrations in do… (voir plus)mains like robotics. Goal-conditioning these policies enables a single generalist policy to capture diverse behaviors contained within an offline dataset. While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-shot to tasks that require conditioning on novel state-goal pairs, i.e. combinatorial generalization. In part, this limitation can be attributed to a lack of temporal consistency in the state representation learned by BC; if temporally related states are encoded to similar latent representations, then the out-of-distribution gap for novel state-goal pairs would be reduced. Hence, encouraging this temporal consistency in the representation space should facilitate combinatorial generalization. Successor representations, which encode the distribution of future states visited from the current state, nicely encapsulate this property. However, previous methods for learning successor representations have relied on contrastive samples, temporal-difference (TD) learning, or both. In this work, we propose a simple yet effective representation learning objective,

2025-06-11

ArXiv (prépublication)

Plasticity as the Mirror of Empowerment

David Abel

Michael Bowling

Andre Barreto

Will Dabney

Shi Dong

Steven Hansen

Anna Harutyunyan

Clare Lyle

Razvan Pascanu

Georgios Piliouras

Doina Precup

Jonathan Richens

Mark Rowland

Tom Schaul

Satinder Singh

2025-05-15

ArXiv (prépublication)

Plasticity as the Mirror of Empowerment

David Abel

Michael Bowling

Andre Barreto

Will Dabney

Shi Dong

Steven Hansen

Anna Harutyunyan

Clare Lyle

Razvan Pascanu

Georgios Piliouras

Doina Precup

Jonathan Richens

Mark Rowland

Tom Schaul

Satinder Singh

2025-05-01

arXiv (publié)

Representation Learning via Non-Contrastive Mutual Information

Zhaohan Daniel Guo

Bernardo Avila Pires

Dale Schuurmans

Bo Dai

2025-04-23

ArXiv (prépublication)

Representation Learning via Non-Contrastive Mutual Information

Zhaohan Daniel Guo

Bernardo Avila Pires

Dale Schuurmans

Bo Dai

2025-04-23

ArXiv (prépublication)

Long Range Navigator (LRN): Extending robot planning horizons beyond metric maps

Matt Schmittle

Rohan Baijal

Nathan Hatch

Rosario Scalise

Mateo Guaman Castro

Sidharth Talia

Byron Boots

S. Srinivasa

2025-04-17

ArXiv (prépublication)

Cracking the Code of Action: A Generative Approach to Affordances for Reinforcement Learning

David Venuto

Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboar… (voir plus)d actions) can require very large amounts of domain-specific expert demonstrations to achieve good performance. Low sample efficiency is often exacerbated in sparse-reward and large-action-space environments, such as a web GUI, where only a few actions are relevant in any given situation. In this work, we consider the low-data regime, with limited or no access to expert behavior. To enable sample-efficient learning, we explore the effect of constraining the action space through intent-based affordances -- i.e., considering in any situation only the subset of actions that achieve a desired outcome. We propose **Code as Generative Affordances**

2025-03-05

ICLR.cc/2025/Workshop/DL4C (publié)

Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning

David Venuto

Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboar… (voir plus)d actions) can require very large amounts of domain-specific expert demonstrations to achieve good performance. Low sample efficiency is often exacerbated in sparse-reward and large-action-space environments, such as a web GUI, where only a few actions are relevant in any given situation. In this work, we consider the low-data regime, with limited or no access to expert behavior. To enable sample-efficient learning, we explore the effect of constraining the action space through *intent-based affordances* -- i.e., considering in any situation only the subset of actions that achieve a desired outcome. We propose **Code as Generative Affordances (

2025-03-05

ICLR.cc/2025/Workshop/DL4C (publié)