Abstract-The planar bipedal testbed MABEL contains springs in its drivetrain for the purpose of enhancing both energy efficiency and agility of dynamic locomotion. While the potential energetic benefits of springs are well documented in the literature, feedback control designs that effectively realize this potential are lacking. In this paper, we extend and apply the methods of virtual constraints and hybrid zero dynamics, originally developed for rigid robots with a single degree of underactuation, to MABEL, a bipedal walker with a novel compliant transmission and multiple degrees of underactuation. A time-invariant feedback controller is designed such that the closed-loop system respects the natural compliance of the open-loop system and realizes exponentially stable walking gaits. Five experiments are presented that highlight different aspects of MABEL and the feedback design method, ranging from basic elements such as stable walking and robustness under perturbations, to energy efficiency and a walking speed of 1.5 m/s (3.4 mph). The experiments also compare two feedback implementations of the virtual constraints, one based on PD control as in (Westervelt et al., 2004), and a second that implements a full hybrid zero dynamics controller. On MABEL, the full hybrid zero dynamics controller yields a much more faithful realization of the desired virtual constraints and was instrumental in achieving more rapid walking.

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This paper presents the design and implementation of a bounding controller for the MIT Cheetah 2 and its experimental results. The paper introduces the architecture of the controller along with the functional roles of its subcomponents. The application of impulse scaling provides feedforward force profiles that automatically adapt across a wide range of speeds. A discrete gait pattern stabilizer maintains the footfall sequence and timing. Continuous feedback is layered to manage balance during the stance phase. Stable hybrid limit cycles are exhibited in simulation using simplified models, and are further validated in untethered three-dimensional bounding experiments. Experiments are conducted both indoors and outdoors on various man-made and natural terrains. The control framework is shown to provide stable bounding in the hardware, at speeds of up to 6.4 m/s and with a minimum total cost of transport of 0.47. These results are unprecedented accomplishments in terms of efficiency and speed in untethered experimental quadruped machines.

Abstract-This paper introduces MABEL, a new platform for the study of bipedal locomotion in robots. One of the purposes of building the mechanism is to explore a novel powertrain design that incorporates compliance, with the objective of improving the power efficiency of the robot, both in steady state operation and in responding to disturbances. A second purpose is to inspire the development of new feedback control algorithms for running on level surfaces and walking on rough terrain. A third motivation for building the robot is science and technology outreach; indeed, it is already included in tours when K-through-12 students visit the College of Engineering at the University of Michigan. MABEL is currently walking at 1.1 m/s on a level surface, and a related monopod at Carnegie Mellon is hopping well, establishing that the testbed has the potential to realize its many objectives.

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Personalized education technologies capable of delivering adaptive interventions could play an important role in addressing the needs of diverse young learners at a critical time of school readiness. We present an innovative personalized social robot learning companion system that utilizes children’s verbal and nonverbal affective cues to modulate their engagement and maximize their long-term learning gains. We propose an affective reinforcement learning approach to train a personalized policy for each student during an educational activity where a child and a robot tell stories to each other. Using the personalized policy, the robot selects stories that are optimized for each child’s engagement and linguistic skill progression. We recruited 67 bilingual and English language learners between the ages of 4–6 years old to participate in a between-subjects study to evaluate our system. Over a three-month deployment in schools, a unique storytelling policy was trained to deliver a personalized story curriculum for each child in the Personalized group. We compared their engagement and learning outcomes to a Non-personalized group with a fixed curriculum robot, and a baseline group that had no robot intervention. In the Personalization condition, our results show that the affective policy successfully personalized to each child to boost their engagement and outcomes with respect to learning and retaining more target words as well as using more target syntax structures as compared to children in the other groups.

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Mindset has been shown to have a large impact on people’s academic, social, and work achievements. A growth mindset, i.e., the belief that success comes from effort and perseverance, is a better indicator of higher achievements as compared to a fixed mindset, i.e., the belief that things are set and cannot be changed. Interventions aimed at promoting a growth mindset in children range from teaching about the brain’s ability to learn and change, to playing computer games that grant brain points for effort rather than success. This work explores a novel paradigm to foster a growth mindset in young children where they play a puzzle solving game with a peer-like social robot. The social robot is fully autonomous and programmed with behaviors suggestive of it having either a growth mindset or a neutral mindset as it plays puzzle games with the child. We measure the mindset of children before and after interacting with the peer-like robot, in addition to measuring their problem solving behavior when faced with a challenging puzzle. We found that children who played with a growth mindset robot 1) self-reported having a stronger growth mindset and 2) tried harder during a challenging task, as compared to children who played with the neutral mindset robot. These results suggest that interacting with peer-like social robot with a growth mindset can promote the same mindset in children.