no code implementations • 16 Oct 2023 • Chengguang Xu, Hieu T. Nguyen, Christopher Amato, Lawson L. S. Wong
Directly transferring SOTA navigation policies trained in simulation to the real world is challenging due to the visual domain gap and the absence of prior knowledge about unseen environments.
no code implementations • 22 Sep 2023 • Linfeng Zhao, Hongyu Li, Taskin Padir, Huaizu Jiang, Lawson L. S. Wong
Learning for robot navigation presents a critical and challenging task.
no code implementations • 17 Jul 2023 • Linfeng Zhao, Owen Howell, Jung Yeon Park, Xupeng Zhu, Robin Walters, Lawson L. S. Wong
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws. These changes, which preserve distance, encompass isometric transformations such as translations, rotations, and reflections, collectively known as the Euclidean group.
no code implementations • 21 Jun 2023 • Ondrej Biza, Skye Thompson, Kishore Reddy Pagidi, Abhinav Kumar, Elise van der Pol, Robin Walters, Thomas Kipf, Jan-Willem van de Meent, Lawson L. S. Wong, Robert Platt
We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration.
no code implementations • 16 Nov 2022 • Dian Wang, Jung Yeon Park, Neel Sortur, Lawson L. S. Wong, Robin Walters, Robert Platt
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture.
no code implementations • 24 Oct 2022 • Linfeng Zhao, Huazhe Xu, Lawson L. S. Wong
To alleviate this issue, we propose to differentiate through the Bellman fixed-point equation to decouple forward and backward passes for Value Iteration Network and its variants, which enables constant backward cost (in planning horizon) and flexible forward budget and helps scale up to large tasks.
no code implementations • 17 Oct 2022 • Jung Yeon Park, Lawson L. S. Wong
On continuous control domains, we evaluate the robustness when starting from different initial states unseen in the demonstration data.
no code implementations • 8 Jun 2022 • Linfeng Zhao, Xupeng Zhu, Lingzhi Kong, Robin Walters, Lawson L. S. Wong
Our implementation is based on VINs and uses steerable convolution networks to incorporate symmetry.
no code implementations • 28 Apr 2022 • Linfeng Zhao, Lingzhi Kong, Robin Walters, Lawson L. S. Wong
Compositional generalization is a critical ability in learning and decision-making.
1 code implementation • 27 Apr 2022 • Ondrej Biza, Robert Platt, Jan-Willem van de Meent, Lawson L. S. Wong, Thomas Kipf
We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms.
1 code implementation • 10 Feb 2022 • Ondrej Biza, Thomas Kipf, David Klee, Robert Platt, Jan-Willem van de Meent, Lawson L. S. Wong
In this paper, we learn to generalize over robotic pick-and-place tasks using object-factored world models, which combat the combinatorial explosion by ensuring that predictions are equivariant to permutations of objects.
no code implementations • 9 Oct 2021 • Seth Pate, Wei Xu, ZiYi Yang, Maxwell Love, Siddarth Ganguri, Lawson L. S. Wong
To enable robots to instruct humans in collaborations, we identify several aspects of language processing that are not commonly studied in this context.
no code implementations • 7 Oct 2021 • David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong
We address this question by proposing a simple measure of reinforcement-learning hardness called the bad-policy density.
no code implementations • 7 Jun 2021 • Chengguang Xu, Christopher Amato, Lawson L. S. Wong
In this work, we propose an approach that leverages a rough 2-D map of the environment to navigate in novel environments without requiring further learning.
1 code implementation • 11 Jan 2021 • Ondrej Biza, Dian Wang, Robert Platt, Jan-Willem van de Meent, Lawson L. S. Wong
This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks.
no code implementations • 1 Jan 2021 • Linfeng Zhao, Lawson L. S. Wong
To learn this ability, we need to efficiently train an agent on environments with a small proportion of training maps and share knowledge effectively across the environments.
1 code implementation • NeurIPS 2020 • Fan Xie, Alexander Chowdhury, M. Clara De Paolis Kaluza, Linfeng Zhao, Lawson L. S. Wong, Rose Yu
Compared to baselines, our model generalizes better and achieves higher success rates on several simulated bimanual robotic manipulation tasks.
1 code implementation • pproximateinference AABI Symposium 2021 • Ondrej Biza, Robert Platt, Jan-Willem van de Meent, Lawson L. S. Wong
In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction.
1 code implementation • WS 2017 • Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands.
no code implementations • ICLR 2018 • Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
Deep neural networks are able to solve tasks across a variety of domains and modalities of data.
1 code implementation • 21 Apr 2017 • Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
In this work, by grounding commands to all the tasks or subtasks available in a hierarchical planning framework, we arrive at a model capable of interpreting language at multiple levels of specificity ranging from coarse to more granular.
no code implementations • 2 Dec 2015 • Lawson L. S. Wong, Thanard Kurutach, Leslie Pack Kaelbling, Tomás Lozano-Pérez
We refer to this attribute-based representation as a world model, and consider how to acquire it via noisy perception and maintain it over time, as objects are added, changed, and removed in the world.