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1. ARCADE: Scalable Demonstration Collection and Generation via Augmented Reality for Imitation Learning

   Yang, Yue; Ikeda, Bryce; Bertasius, Gedas; Szafir, Daniel (October 2024, 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   Robot Imitation Learning (IL) is a crucial technique in robot learning, where agents learn by mimicking human demonstrations. However, IL encounters scalability challenges stemming from both non-user-friendly demonstration collection methods and the extensive time required to amass a sufficient number of demonstrations for effective training. In response, we introduce the Augmented Reality for Collection and generAtion of DEmonstrations (ARCADE) framework, designed to scale up demonstration collection for robot manipulation tasks. Our framework combines two key capabilities: 1) it leverages AR to make demonstration collection as simple as users performing daily tasks using their hands, and 2) it enables the automatic generation of additional synthetic demonstrations from a single human-derived demonstration, significantly reducing user effort and time. We assess ARCADE's performance on a real Fetch robot across three robotics tasks: 3-Waypoints-Reach, Push, and Pick-And-Place. Using our framework, we were able to rapidly train a policy using vanilla Behavioral Cloning (BC), a classic IL algorithm, which excelled across these three tasks. We also deploy ARCADE on a real household task, Pouring-Water, achieving an 80% success rate. 

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2. HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration

   Xingyu Liu; Deepak Pathak; Kris M. Kitani (October 2022, Conference on Robot Learning)

   The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial two-finger-gripper robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to a target commercial robot. 

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3. O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning

   Kaichun Mo, Yuzhe Qin (November 2021, Conference on Robot Learning (CoRL) 2021)

   Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and planning tasks. There is a rich space of object-object interaction scenarios in our daily life, such as placing an object on a messy tabletop, fitting an object inside a drawer, pushing an object using a tool, etc. In this paper, we propose a unified affordance learning framework to learn object-object interaction for various tasks. By constructing four object-object interaction task environments using physical simulation (SAPIEN) and thousands of ShapeNet models with rich geometric diversity, we are able to conduct large-scale object-object affordance learning without the need for human annotations or demonstrations. At the core of technical contribution, we propose an object-kernel point convolution network to reason about detailed interaction between two objects. Experiments on large-scale synthetic data and real-world data prove the effectiveness of the proposed approach. 

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4. Characterizing Continuous Manipulation Families for Dexterous Soft Robot Hands

   Sun, J.; King, J.; Pollard, N. (April 2021, Frontiers in robotics and AI)

   null (Ed.)

   There has been an explosion of ideas in soft robotics over the past decade, resulting in unprecedented opportunities for end effector design. Soft robot hands offer benefits of low-cost, compliance, and customized design, with the promise of dexterity and robustness. The space of opportunities is vast and exciting. However, new tools are needed to understand the capabilities of such manipulators and to facilitate manipulation planning with soft manipulators that exhibit free-form deformations. To address this challenge, we introduce a sampling based approach to discover and model continuous families of manipulations for soft robot hands. We give an overview of the soft foam robots in production in our lab and describe novel algorithms developed to characterize manipulation families for such robots. Our approach consists of sampling a space of manipulation actions, constructing Gaussian Mixture Model representations covering successful regions, and refining the results to create continuous successful regions representing the manipulation family. The space of manipulation actions is very high dimensional; we consider models with and without dimensionality reduction and provide a rigorous approach to compare models across different dimensions by comparing coverage of an unbiased test dataset in the full dimensional parameter space. Results show that some dimensionality reduction is typically useful in populating the models, but without our technique, the amount of dimensionality reduction to use is difficult to predict ahead of time and can depend on the hand and task. The models we produce can be used to plan and carry out successful, robust manipulation actions and to compare competing robot hand designs. 

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5. Remote Telemanipulation with Adapting Viewpoints in Visually Complex Environments

   https://doi.org/10.15607/RSS.2019.XV.068  

   Rakita, Daniel; Mutlu, Bilge; Gleicher, Michael (June 2019, Robotics: Science and Systems XV)

   In this paper, we introduce a novel method to support remote telemanipulation tasks in complex environments by providing operators with an enhanced view of the task environment. Our method features a novel viewpoint adjustment algorithm designed to automatically mitigate occlusions caused by workspace geometry, supports visual exploration to provide operators with situation awareness in the remote environment, and mediates context-specific visual challenges by making viewpoint adjustments based on sparse input from the user. Our method builds on the dynamic camera telemanipulation viewing paradigm, where a user controls a manipulation robot, and a camera-in-hand robot alongside the manipulation robot servos to provide a sufficient view of the remote environment. We discuss the real-time motion optimization formulation used to arbitrate the various objectives in our shared-control-based method, particularly highlighting how our occlusion avoidance and viewpoint adaptation approaches fit within this framework. We present results from an empirical evaluation of our proposed occlusion avoidance approach as well as a user study that compares our telemanipulation shared-control method against alternative telemanipulation approaches. We discuss the implications of our work for future shared-control research and robotics applications. 

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