Author name cluster

Ben Evans

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5 papers

1 author row

ICML Conference 2024 Conference Paper

PcLast: Discovering Plannable Continuous Latent States

Anurag Koul
Shivakanth Sujit
Shaoru Chen
Ben Evans
Lili Wu
Byron Xu
Rajan Chari
Riashat Islam

Goal-conditioned planning benefits from learned low-dimensional representations of rich observations. While compact latent representations typically learned from variational autoencoders or inverse dynamics enable goal-conditioned decision making, they ignore state reachability, hampering their performance. In this paper, we learn a representation that associates reachable states together for effective planning and goal-conditioned policy learning. We first learn a latent representation with multi-step inverse dynamics (to remove distracting information), and then transform this representation to associate reachable states together in $\ell_2$ space. Our proposals are rigorously tested in various simulation testbeds. Numerical results in reward-based settings show significant improvements in sampling efficiency. Further, in reward-free settings this approach yields layered state abstractions that enable computationally efficient hierarchical planning for reaching ad hoc goals with zero additional samples.

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ICRA Conference 2024 Conference Paper

See to Touch: Learning Tactile Dexterity through Visual Incentives

Irmak Güzey
Yinlong Dai
Ben Evans
Soumith Chintala
Lerrel Pinto

Equipping multi-fingered robots with tactile sensing is crucial for achieving the precise, contact-rich, and dexterous manipulation that humans excel at. However, relying solely on tactile sensing fails to provide adequate cues for reasoning about objects’ spatial configurations, limiting the ability to correct errors and adapt to changing situations. In this paper, we present Tactile Adaptation from Visual Incentives (TAVI), a new framework that enhances tactile-based dexterity by optimizing dexterous policies using vision-based rewards. First, we use a contrastive-based objective to learn visual representations. Next, we construct a reward function using these visual representations through optimal-transport based matching on one human demonstration. Finally, we use online reinforcement learning on our robot to optimize tactile-based policies that maximize the visual reward. On six challenging tasks, such as peg pick-and-place, unstacking bowls, and flipping slender objects, TAVI achieves a success rate of 73% using our four-fingered Allegro robot hand. The increase in performance is 108% higher than policies using tactile and vision-based rewards and 135% higher than policies without tactile observational input. Robot videos are best viewed on our project website: https://see-to-touch.github.io/.

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ICRA Conference 2023 Conference Paper

Dexterous Imitation Made Easy: A Learning-Based Framework for Efficient Dexterous Manipulation

Sridhar Pandian Arunachalam
Sneha Silwal
Ben Evans
Lerrel Pinto

Optimizing behaviors for dexterous manipulation has been a longstanding challenge in robotics, with a variety of methods from model-based control to model-free reinforcement learning having been previously explored in literature. Such prior work often require extensive trial-and-error training along with task-specific tuning of reward functions, which makes applying dexterous manipulation for general purpose problems quite impractical. A sample-efficient and practical alternate to trial-and-error learning is imitation learning. However, collecting and learning from demonstrations in dexterous manipulation is quite challenging due to the high-dimensional action-space involved with multi-finger control. In this work, we propose ‘Dexterous Imitation Made Easy’ (DIME) a new imitation learning framework for dexterous manipulation. DIME only requires a single RGB camera that observes a human operator to teleoperate a robotic hand. Once demonstrations are collected, DIME employs state-of-the-art imitation learning methods to train dexterous manipulation policies. On real robot benchmarks we demonstrate that DIME can be used to solve complex, in-hand manipulation tasks such as ‘flipping’, ‘spinning’, and ‘rotating’ objects with just 30 demonstrations and no additional robot training. Our code, pre-collected demonstrations, and robot videos are publicly available at: https://nyu-robot-learning.github.io/dime.

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ICLR Conference 2022 Conference Paper

BAM: Bayes with Adaptive Memory

Josue Nassar
Jennifer Rogers Brennan
Ben Evans
Kendall Lowrey

Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non-stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common solution when learning in changing environments is to discard/downweight past data; however, this simple mechanism of "forgetting" fails to account for the fact that many real-world environments involve revisiting similar states. We propose a new framework, Bayes with Adaptive Memory (BAM), that takes advantage of past experience by allowing the agent to choose which past observations to remember and which to forget. We demonstrate that BAM generalizes many popular Bayesian update rules for non-stationary environments. Through a variety of experiments, we demonstrate the ability of BAM to continuously adapt in an ever-changing world.

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ICRA Conference 2022 Conference Paper

Context is Everything: Implicit Identification for Dynamics Adaptation

Ben Evans
Abitha Thankaraj
Lerrel Pinto

Understanding environment dynamics is necessary for robots to act safely and optimally in the world. In realistic scenarios, dynamics are non-stationary and the causal variables such as environment parameters cannot necessarily be precisely measured or inferred, even during training. We propose Implicit Identification for Dynamics Adaptation (IIDA), a simple method to allow predictive models to adapt to changing environment dynamics. IIDA assumes no access to the true variations in the world and instead implicitly infers properties of the environment from a small amount of contextual data. We demonstrate IIDA's ability to perform well in unseen environments through a suite of simulated experiments on MuJoCo environments and a real robot dynamic sliding task. In general, IIDA significantly reduces model error and results in higher task performance over commonly used methods. Our code, video of the method, and latest paper is available here https://bennevans.github.io/icra-iida/

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