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1. Quadrupedal Robotic Walking on Sloped Terrains via Exact Decomposition into Coupled Bipedal Robots

   https://doi.org/10.1109/IROS45743.2020.9341181  

   Ma, Wen-Loong ; Csomay-Shanklin, Noel ; Ames, Aaron D. ( October 2020 , IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   null (Ed.)

   Can we design motion primitives for complex legged systems uniformly for different terrain types without neglecting modeling details? This paper presents a method for rapidly generating quadrupedal locomotion on sloped terrains-from modeling to gait generation, to hardware demonstration. At the core of this approach is the observation that a quadrupedal robot can be exactly decomposed into coupled bipedal robots. Formally, this is represented through the framework of coupled control systems, wherein isolated subsystems interact through coupling constraints. We demonstrate this concept in the context of quadrupeds and use it to reduce the gait planning problem for uneven terrains to bipedal walking generation via hybrid zero dynamics. This reduction method allows for the formulation of a nonlinear optimization problem that leverages low-dimensional bipedal representations to generate dynamic walking gaits on slopes for the full-order quadrupedal robot dynamics. The result is the ability to rapidly generate quadrupedal walking gaits on a variety of slopes. We demonstrate these walking behaviors on the Vision 60 quadrupedal robot; in simulation, via walking on a range of sloped terrains of 13°, 15°, 20°, 25°, and, experimentally, through the successful locomotion of 13° and 20° ~ 25° sloped outdoor grasslands. 

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2. A Dynamics Simulator for Soft Growing Robots

   https://doi.org/10.1109/ICRA48506.2021.9561420  

   Jitosho, Rianna ; Agharese, Nathaniel ; Okamura, Allison ; Manchester, Zac ( May 2021 , IEEE International Conference on Robotics and Automation)

   Simulating soft robots in cluttered environments remains an open problem due to the challenge of capturing complex dynamics and interactions with the environment. Fur- thermore, fast simulation is desired for quickly exploring robot behaviors in the context of motion planning. In this paper, we examine a particular class of inflated-beam soft growing robots called “vine robots,” and present a dynamics simulator that captures general behaviors, handles robot-object interactions, and runs faster than real time. The simulator framework uses a simplified multi-link, rigid-body model with contact constraints. To bridge the sim-to-real gap, we develop methods for fitting model parameters based on video data of a robot in motion and in contact with an environment. We provide examples of simulations, including several with fit parameters, to show the qualitative and quantitative agreement between simulated and real behaviors. Our work demonstrates the capabilities of this high-speed dynamics simulator and its potential for use in the control of soft robots. 

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3. RealTHASC—a cyber-physical XR testbed for AI-supported real-time human autonomous systems collaborations

   https://doi.org/10.3389/frvir.2023.1210211  

   Paradise, Andre ; Surve, Sushrut ; Menezes, Jovan C. ; Gupta, Madhav ; Bisht, Vaibhav ; Jang, Kyung Rak ; Liu, Cong ; Qiu, Suming ; Dong, Junyi ; Shin, Jane ; et al ( December 2023 , Frontiers in Virtual Reality)

   Gonzalez, D. (Ed.)

   Today’s research on human-robot teaming requires the ability to test artificial intelligence (AI) algorithms for perception and decision-making in complex real-world environments. Field experiments, also referred to as experiments “in the wild,” do not provide the level of detailed ground truth necessary for thorough performance comparisons and validation. Experiments on pre-recorded real-world data sets are also significantly limited in their usefulness because they do not allow researchers to test the effectiveness of active robot perception and control or decision strategies in the loop. Additionally, research on large human-robot teams requires tests and experiments that are too costly even for the industry and may result in considerable time losses when experiments go awry. The novel Real-Time Human Autonomous Systems Collaborations (RealTHASC) facility at Cornell University interfaces real and virtual robots and humans with photorealistic simulated environments by implementing new concepts for the seamless integration of wearable sensors, motion capture, physics-based simulations, robot hardware and virtual reality (VR). The result is an extended reality (XR) testbed by which real robots and humans in the laboratory are able to experience virtual worlds, inclusive of virtual agents, through real-time visual feedback and interaction. VR body tracking by DeepMotion is employed in conjunction with the OptiTrack motion capture system to transfer every human subject and robot in the real physical laboratory space into a synthetic virtual environment, thereby constructing corresponding human/robot avatars that not only mimic the behaviors of the real agents but also experience the virtual world through virtual sensors and transmit the sensor data back to the real human/robot agent, all in real time. New cross-domain synthetic environments are created in RealTHASC using Unreal Engine™, bridging the simulation-to-reality gap and allowing for the inclusion of underwater/ground/aerial autonomous vehicles, each equipped with a multi-modal sensor suite. The experimental capabilities offered by RealTHASC are demonstrated through three case studies showcasing mixed real/virtual human/robot interactions in diverse domains, leveraging and complementing the benefits of experimentation in simulation and in the real world.

    

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4. Inferring Task-Space Central Pattern Generator Parameters for Closed-loop Control of Underactuated Robots

   https://doi.org/10.1109/ICRA40945.2020.9196957  

   Kent, Nathan D. ; Bhirangi, Raunaq M. ; Travers, Matthew ; Howard, Thomas M. ( May 2020 , 2020 IEEE International Conference on Robotics and Automation (ICRA))

   The complexity associated with the control of highly-articulated legged robots scales quickly as the number of joints increases. Traditional approaches to the control of these robots are often impractical for many real-time applications. This work thus presents a novel sampling-based planning ap- proach for highly-articulated robots that utilizes a probabilistic graphical model (PGM) to infer in real-time how to optimally modify goal-driven, locomotive behaviors for use in closed-loop control. Locomotive behaviors are quantified in terms of the parameters associated with a network of neural oscillators, or rather a central pattern generator (CPG). For the first time, we show that the PGM can be used to optimally modulate different behaviors in real-time (i.e., to select of optimal choice of parameter values across the CPG model) in response to changes both in the local environment and in the desired control signal. The PGM is trained offline using a library of optimal behaviors that are generated using a gradient-free optimization framework. 

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5. Real2Sim2Real Transfer for Control of Cable-driven Robots via a Differentiable Physics Engine

   Wang, K. ; Johnson, William R. ; Lu, S. ; Huang, X. ; Booth, J. ; Kramer-Bottiglio, R. ; Aanjaneya, M. ; Bekris, K. E. ( October 2023 , IEEE/RSJ International Conference on Intelligent Robots and Systems)

   Tensegrity robots, composed of rigid rods and flexible cables, exhibit high strength-to-weight ratios and significant deformations, which enable them to navigate unstructured terrains and survive harsh impacts. They are hard to control, however, due to high dimensionality, complex dynamics, and a coupled architecture. Physics-based simulation is a promising avenue for developing locomotion policies that can be transferred to real robots. Nevertheless, modeling tensegrity robots is a complex task due to a substantial sim2real gap. To address this issue, this paper describes a Real2Sim2Real (R2S2R) strategy for tensegrity robots. This strategy is based on a differentiable physics engine that can be trained given limited data from a real robot. These data include offline measurements of physical properties, such as mass and geometry for various robot components, and the observation of a trajectory using a random control policy. With the data from the real robot, the engine can be iteratively refined and used to discover locomotion policies that are directly transferable to the real robot. Beyond the R2S2R pipeline, key contributions of this work include computing non-zero gradients at contact points, a loss function for matching tensegrity locomotion gaits, and a trajectory segmentation technique that avoids conflicts in gradient evaluation during training. Multiple iterations of the R2S2R process are demonstrated and evaluated on a real 3-bar tensegrity robot. 

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