Crowdsourcing Task Traces for Service Robotics

Demonstration is an effective end-user development paradigm for teaching robots how to perform new tasks. In this paper, we posit that demonstration is useful not only as a teaching tool, but also as a way to understand and assist end-user developers in thinking about a task at hand. As a first step toward gaining this understanding, we constructed a lightweight web interface to crowdsource step-by-step instructions of common household tasks, leveraging the imaginations and past experiences of potential end-user developers. As evidence of the utility of our interface, we deployed the interface on Amazon Mechanical Turk and collected 207 task traces that span 18 different task categories. We describe our vision for how these task traces can be operationalized as task models within end-user development tools and provide a roadmap for future work.

Multiple Ways of Working with Users to Develop Physically Assistive Robots

Despite the growth of physically assistive robotics (PAR) research over the last decade, nearly half of PAR user studies do not involve participants with the target disabilities. There are several reasons for this -- recruitment challenges, small sample sizes, and transportation logistics -- all influenced by systemic barriers that people with disabilities face. However, it is well-established that working with end-users results in technology that better addresses their needs and integrates with their lived circumstances. In this paper, we reflect on multiple approaches we have taken to working with people with motor impairments across the design, development, and evaluation of three PAR projects: (a) assistive feeding with a robot arm; (b) assistive teleoperation with a mobile manipulator; and (c) shared control with a robot arm. We discuss these approaches to working with users along three dimensions -- individual vs. community-level insight, logistic burden on end-users vs. researchers, and benefit to researchers vs. community -- and share recommendations for how other PAR researchers can incorporate users into their work.

Fast Explicit-Input Assistance for Teleoperation in Clutter

The performance of prediction-based assistance for robot teleoperation degrades in unseen or goal-rich environments due to incorrect or quickly-changing intent inferences. Poor predictions can confuse operators or cause them to change their control input to implicitly signal their goal, resulting in unnatural movement. We present a new assistance algorithm and interface for robotic manipulation where an operator can explicitly communicate a manipulation goal by pointing the end-effector. Rapid optimization and parallel collision checking in a local region around the pointing target enable direct, interactive control over grasp and place pose candidates. We compare the explicit pointing interface to an implicit inference-based assistance scheme in a within-subjects user study (N=20) where participants teleoperate a simulated robot to complete a multi-step singulation and stacking task in cluttered environments. We find that operators prefer the explicit interface, which improved completion time, pick and place success rates, and NASA TLX scores. Our code is available at https://github.com/NVlabs/fast-explicit-teleop

Evaluating Customization of Remote Tele-operation Interfaces for Assistive Robots

Mobile manipulator platforms, like the Stretch RE1 robot, make the promise of in-home robotic assistance feasible. For people with severe physical limitations, like those with quadriplegia, the ability to tele-operate these robots themselves means that they can perform physical tasks they cannot otherwise do themselves, thereby increasing their level of independence. In order for users with physical limitations to operate these robots, their interfaces must be accessible and cater to the specific needs of all users. As physical limitations vary amongst users, it is difficult to make a single interface that will accommodate all users. Instead, such interfaces should be customizable to each individual user. In this paper we explore the value of customization of a browser-based interface for tele-operating the Stretch RE1 robot. More specifically, we evaluate the usability and effectiveness of a customized interface in comparison to the default interface configurations from prior work. We present a user study involving participants with motor impairments (N=10) and without motor impairments, who could serve as a caregiver, (N=13) that use the robot to perform mobile manipulation tasks in a real kitchen environment. Our study demonstrates that no single interface configuration satisfies all users' needs and preferences. Users perform better when using the customized interface for navigation, but not for manipulation due to higher complexity of learning to manipulate through the robot. All participants are able to use the robot to complete all tasks and participants with motor impairments believe that having the robot in their home would make them more independent.

Sketching Robot Programs On the Fly

Service robots for personal use in the home and the workplace require end-user development solutions for swiftly scripting robot tasks as the need arises. Many existing solutions preserve ease, efficiency, and convenience through simple programming interfaces or by restricting task complexity. Others facilitate meticulous task design but often do so at the expense of simplicity and efficiency. There is a need for robot programming solutions that reconcile the complexity of robotics with the on-the-fly goals of end-user development. In response to this need, we present a novel, multimodal, and on-the-fly development system, Tabula. Inspired by a formative design study with a prototype, Tabula leverages a combination of spoken language for specifying the core of a robot task and sketching for contextualizing the core. The result is that developers can script partial, sloppy versions of robot programs to be completed and refined by a program synthesizer. Lastly, we demonstrate our anticipated use cases of Tabula via a set of application scenarios.

Share with Me: A Study on a Social Robot Collecting Mental Health Data

Social robots have been used to assist with mental well-being in various ways such as to help children with autism improve on their social skills and executive functioning such as joint attention and bodily awareness. They are also used to help older adults by reducing feelings of isolation and loneliness, as well as supporting mental well-being of teens and children. However, existing work in this sphere has only shown support for mental health through social robots by responding interactively to human activity to help them learn relevant skills. We hypothesize that humans can also get help from social robots in mental well-being by releasing or sharing their mental health data with the social robots. In this paper, we present a human-robot interaction (HRI) study to evaluate this hypothesis. During the five-day study, a total of fifty-five (n=55) participants shared their in-the-moment mood and stress levels with a social robot. We saw a majority of positive results indicating it is worth conducting future work in this direction, and the potential of social robots to largely support mental well-being.

HandoverSim: A Simulation Framework and Benchmark for Human-to-Robot Object Handovers

We introduce a new simulation benchmark "HandoverSim" for human-to-robot object handovers. To simulate the giver's motion, we leverage a recent motion capture dataset of hand grasping of objects. We create training and evaluation environments for the receiver with standardized protocols and metrics. We analyze the performance of a set of baselines and show a correlation with a real-world evaluation. Code is open sourced at https://handover-sim.github.io.

Model Predictive Control for Fluid Human-to-Robot Handovers

Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handovers of unknown objects by using learning-based grasp generators. However, how to responsively generate smooth motions to take an object from a human is still an open question. Specifically, planning motions that take human comfort into account is not a part of the human-robot handover process in most prior works. In this paper, we propose to generate smooth motions via an efficient model-predictive control (MPC) framework that integrates perception and complex domain-specific constraints into the optimization problem. We introduce a learning-based grasp reachability model to select candidate grasps which maximize the robot's manipulability, giving it more freedom to satisfy these constraints. Finally, we integrate a neural net force/torque classifier that detects contact events from noisy data. We conducted human-to-robot handover experiments on a diverse set of objects with several users (N=4) and performed a systematic evaluation of each module. The study shows that the users preferred our MPC approach over the baseline system by a large margin. More results and videos are available at https://sites.google.com/nvidia.com/mpc-for-handover.

Assistive Tele-op: Leveraging Transformers to Collect Robotic Task Demonstrations

Sharing autonomy between robots and human operators could facilitate data collection of robotic task demonstrations to continuously improve learned models. Yet, the means to communicate intent and reason about the future are disparate between humans and robots. We present Assistive Tele-op, a virtual reality (VR) system for collecting robot task demonstrations that displays an autonomous trajectory forecast to communicate the robot's intent. As the robot moves, the user can switch between autonomous and manual control when desired. This allows users to collect task demonstrations with both a high success rate and with greater ease than manual teleoperation systems. Our system is powered by transformers, which can provide a window of potential states and actions far into the future -- with almost no added computation time. A key insight is that human intent can be injected at any location within the transformer sequence if the user decides that the model-predicted actions are inappropriate. At every time step, the user can (1) do nothing and allow autonomous operation to continue while observing the robot's future plan sequence, or (2) take over and momentarily prescribe a different set of actions to nudge the model back on track. We host the videos and other supplementary material at https://sites.google.com/view/assistive-teleop.

Learning Perceptual Concepts by Bootstrapping from Human Queries

Robots need to be able to learn concepts from their users in order to adapt their capabilities to each user's unique task. But when the robot operates on high-dimensional inputs, like images or point clouds, this is impractical: the robot needs an unrealistic amount of human effort to learn the new concept. To address this challenge, we propose a new approach whereby the robot learns a low-dimensional variant of the concept and uses it to generate a larger data set for learning the concept in the high-dimensional space. This lets it take advantage of semantically meaningful privileged information only accessible at training time, like object poses and bounding boxes, that allows for richer human interaction to speed up learning. We evaluate our approach by learning prepositional concepts that describe object state or multi-object relationships, like above, near, or aligned, which are key to user specification of task goals and execution constraints for robots. Using a simulated human, we show that our approach improves sample complexity when compared to learning concepts directly in the high-dimensional space. We also demonstrate the utility of the learned concepts in motion planning tasks on a 7-DoF Franka Panda robot.