Ben Evans

Sridhar Pandian Arunachalam, Sneha Silwal, Ben Evans et al.

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. Perhaps one of the most powerful techniques to learn complex manipulation strategies is imitation learning. However, collecting and learning from demonstrations in dexterous manipulation is quite challenging. The complex, high-dimensional action-space involved with multi-finger control often leads to poor sample efficiency of learning-based methods. 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 to observe a human operator and teleoperate our robotic hand. Once demonstrations are collected, DIME employs standard imitation learning methods to train dexterous manipulation policies. On both simulation and 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 the Allegro hand. Our framework along with pre-collected demonstrations is publicly available at https://nyu-robot-learning.github.io/dime.

Irmak Guzey, Ben Evans, Soumith Chintala et al.

Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics. Most prominent work in this area focuses on learning controllers or policies that either operate on visual observations or state estimates derived from vision. However, such methods perform poorly on fine-grained manipulation tasks that require reasoning about contact forces or about objects occluded by the hand itself. In this work, we present T-Dex, a new approach for tactile-based dexterity, that operates in two phases. In the first phase, we collect 2.5 hours of play data, which is used to train self-supervised tactile encoders. This is necessary to bring high-dimensional tactile readings to a lower-dimensional embedding. In the second phase, given a handful of demonstrations for a dexterous task, we learn non-parametric policies that combine the tactile observations with visual ones. Across five challenging dexterous tasks, we show that our tactile-based dexterity models outperform purely vision and torque-based models by an average of 1.7X. Finally, we provide a detailed analysis on factors critical to T-Dex including the importance of play data, architectures, and representation learning.

Irmak Guzey, Yinlong Dai, Ben Evans et al.

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/.

Jakub Vincalek, Sean Walton, Ben Evans

Genetic algorithms have been developed for decades by researchers in academia and perform well in engineering applications, yet their uptake in industry remains limited. In order to understand why this is the case, the opinions of users of engineering design tools were gathered. The results from a survey showing the attitudes of engineers and students with design experience with respect to optimisation algorithms are presented. A survey was designed to answer two research questions: To what extent is there a pre-existing sentiment (negative or positive) among students, engineers, and managers towards genetic algorithm-based design? and What are the requirements of practitioners with regards to design optimisation and the design optimisation process? A total of 23 participants (N = 23) took part in the 3-part mixed methods survey. Thematic analysis was conducted on the open-ended questions. A common thread throughout participants responses is that there is a question of trust towards genetic algorithms within industry. Perhaps surprising is that the key to gaining this trust is not producing good results, but creating algorithms which explain the process they take in reaching a result. Participants have expressed a desire to continue to remain in the design loop. This is at odds with the motivation of a portion of the genetic algorithms community of removing humans from the loop. It is clear we need to take a different approach to increase industrial uptake. Based on this, the following recommendations have been made to increase their use in industry: an increase of transparency and explainability of genetic algorithms, an increased focus on user experience, better communication between developers and engineers, and visualising algorithm behaviour.