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1. Examining the Effects of Anticipatory Robot Assistance on Human Decision Making

   https://doi.org/10.1007/978-3-030-62056-1_49  

   Newman, B.A. ; Biswas, A ; Ahuja, S. ; Girdhar, S. ; Kitani, K.K. ; Admoni, H. ( November 2020 , International Conference on Social Robotics (ICSR))

   Wagner, A.R. ; null (Ed.)

   Collaborative robots that provide anticipatory assistance are able to help people complete tasks more quickly. As anticipatory assistance is provided before help is explicitly requested, there is a chance that this action itself will influence the person’s future decisions in the task. In this work, we investigate whether a robot’s anticipatory assistance can drive people to make choices different from those they would otherwise make. Such a study requires measuring intent, which itself could modify intent, resulting in an observer paradox. To combat this, we carefully designed an experiment to avoid this effect. We considered several mitigations such as the careful choice of which human behavioral signals we use to measure intent and designing unobtrusive ways to obtain these signals. We conducted a user study (𝑁=99) in which participants completed a collaborative object retrieval task: users selected an object and a robot arm retrieved it for them. The robot predicted the user’s object selection from eye gaze in advance of their explicit selection, and then provided either collaborative anticipation (moving toward the predicted object), adversarial anticipation (moving away from the predicted object), or no anticipation (no movement, control condition). We found trends and participant comments suggesting people’s decision making changes in the presence of a robot anticipatory motion and this change differs depending on the robot’s anticipation strategy. 

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2. Highly parallelized data-driven MPC for minimal intervention shared control

   https://doi.org/10.15607/RSS.2019.XV.008  

   Broad, A. ; Murphey, T. ; Argall, B. ( June 2019 , Robotics: science and systems)

   We present a shared control paradigm that improves a user’s ability to operate complex, dynamic systems in potentially dangerous environments without a priori knowledge of the user’s objective. In this paradigm, the role of the autonomous partner is to improve the general safety of the system without constraining the user’s ability to achieve unspecified behaviors. Our approach relies on a data-driven,model-based representation of the joint human-machine system to evaluate, in parallel, a significant number of potential inputs that the user may wish to provide. These samples are used to (1)predict the safety of the system over a receding horizon, and (2)minimize the influence of the autonomous partner. The resulting shared control algorithm maximizes the authority allocated to the human partner to improve their sense of agency, while improving safety. We evaluate the efficacy of our shared control algorithm with a human subjects study (n=20) conducted in two simulated environments: a balance bot and a race car. During the experiment, users are free to operate each system however they would like (i.e., there is no specified task) and are only asked to try to avoid unsafe regions of the state space. Using modern computational resources (i.e., GPUs) our approach is able to consider more than 10,000 potential trajectories at each time step in a control loop running at 100Hz for the balance bot and 60Hzfor the race car. The results of the study show that our shared control paradigm improves system safety without knowledge of the user’s goal, while maintaining high-levels of user satisfaction and low-levels of frustration. Our code is available online athttps://github.com/asbroad/mpmisharedcontrol. 

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3. Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance

   https://doi.org/10.15607/RSS.2018.XIV.046  

   Kalinowska, Aleksandra ; Fitzsimons, Kathleen ; Dewald, Julius ; Murphey, Todd ( June 2018 , Robotics: science and systems)

   We propose a novel criterion for evaluating user input for human-robot interfaces for known tasks. We use the mode insertion gradient (MIG)—a tool from hybrid control theory—as a filtering criterion that instantaneously assesses the impact of user actions on a dynamic system over a time window into the future. As a result, the filter is permissive to many chosen strategies, minimally engaging, and skill-sensitive—qualities desired when evaluating human actions. Through a human study with 28 healthy volunteers, we show that the criterion exhibits a low, but significant, negative correlation between skill level, as estimated from task-specific measures in unassisted trials, and the rate of controller intervention during assistance. Moreover, a MIG-based filter can be utilized to create a shared control scheme for training or assistance. In the human study, we observe a substantial training effect when using a MIG-based filter to perform cart-pendulum inversion, particularly when comparing improvement via the RMS error measure. Using simulation of a controlled spring-loaded inverted pendulum (SLIP) as a test case, we observe that the MIG criterion could be used for assistance to guarantee either task completion or safety of a joint human-robot system, while maintaining the system’s flexibility with respect to user-chosen strategies. 

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4. Crash Prediction Using Deep Learning in a Disorienting Spaceflight Analog Balancing Task

   https://doi.org/10.3389/fphys.2022.806357  

   Wang, Yonglin ; Tang, Jie ; Vimal, Vivekanand Pandey ; Lackner, James R. ; DiZio, Paul ; Hong, Pengyu ( January 2022 , Frontiers in Physiology)

   Were astronauts forced to land on the surface of Mars using manual control of their vehicle, they would not have familiar gravitational cues because Mars’ gravity is only 0.38 g. They could become susceptible to spatial disorientation, potentially causing mission ending crashes. In our earlier studies, we secured blindfolded participants into a Multi-Axis Rotation System (MARS) device that was programmed to behave like an inverted pendulum. Participants used a joystick to stabilize around the balance point. We created a spaceflight analog condition by having participants dynamically balance in the horizontal roll plane, where they did not tilt relative to the gravitational vertical and therefore could not use gravitational cues to determine their position. We found 90% of participants in our spaceflight analog condition reported spatial disorientation and all of them showed it in their data. There was a high rate of crashing into boundaries that were set at ± 60 ° from the balance point. Our goal was to see whether we could use deep learning to predict the occurrence of crashes before they happened. We used stacked gated recurrent units (GRU) to predict crash events 800 ms in advance with an AUC (area under the curve) value of 99%. When we prioritized reducing false negatives we found it resulted in more false positives. We found that false negatives occurred when participants made destabilizing joystick deflections that rapidly moved the MARS away from the balance point. These unpredictable destabilizing joystick deflections, which occurred in the duration of time after the input data, are likely a result of spatial disorientation. If our model could work in real time, we calculated that immediate human action would result in the prevention of 80.7% of crashes, however, if we accounted for human reaction times (∼400 ms), only 30.3% of crashes could be prevented, suggesting that one solution could be an AI taking temporary control of the spacecraft during these moments. 

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5. Design of an Intent Recognition System for Dynamic, Rapid Motions in Unstructured Environments

   https://doi.org/10.1115/1.4051140  

   Moolchandani, Pooja R. ; Mazumdar, Anirban ; Young, Aaron J. ( January 2022 , ASME Letters in Dynamic Systems and Control)

   null (Ed.)

   Abstract In this study, we developed an offline, hierarchical intent recognition system for inferring the timing and direction of motion intent of a human operator when operating in an unstructured environment. There has been an increasing demand for robot agents to assist in these dynamic, rapid motions that are constantly evolving and require quick, accurate estimation of a user’s direction of travel. An experiment was conducted in a motion capture space with six subjects performing threat evasion in eight directions, and their mechanical and neuromuscular signals were recorded for use in our intent recognition system (XGBoost). Investigated against current, analytical methods, our system demonstrated superior performance with quicker direction of travel estimation occurring 140 ms earlier in the movement and a 11.6 deg reduction of error. The results showed that we could also predict the start of the movement 100 ms prior to the actual, thus allowing any physical systems to start up. Our direction estimation had an optimal performance of 8.8 deg, or 2.4% of the 360 deg range of travel, using three-axis kinetic data. The performance of other sensors and their combinations indicate that there are additional possibilities to obtain low estimation error. These findings are promising as they can be used to inform the design of a wearable robot aimed at assisting users in dynamic motions, while in environments with oncoming threats. 

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