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1. Grasping Fragile Objects Using A Stress-Minimization Metric

   Pan, Zherong; Gao, Xifeng; Manocha, Dinesh (January 2020, International Conference on Robotics and Automation (ICRA 2020))

   We present a new method to generate optimal grasps for brittle and fragile objects using a novel stressminimization (SM) metric. Our approach is designed for objects that are composed of homogeneous isotopic materials. Our SM metric measures the maximal resistible external wrenches that would not result in fractures in the target objects. In this paper, we propose methods to compute our new metric. We also use our SM metric to design optimal grasp planning algorithms. Finally, we compare the performance of our metric and conventional grasp metrics, including Q1, Q∞, QG11, QMSV , QV EW . Our experiments show that our SM metric takes into account the material characteristics and object shapes to indicate the fragile regions, where prior methods may not work well. We also show that the computational cost of our SM metric is on par with prior methods. Finally, we show that grasp planners guided by our metric can lower the probability of breaking target objects. 

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2. Mirror-Brush Illusion: Creating phantom tactile percepts on intact limbs

   https://doi.org/10.1109/TOH.2023.3279012  

   Singhala, Mohit; Brown, Jeremy D. (January 2023, IEEE Transactions on Haptics)

   Haptic illusions provide unique insights into how we model our bodies separate from our environment. Popular illusions like the rubber-hand illusion and mirror-box illusion have demonstrated that we can adapt the internal representations of our limbs in response to visuo-haptic conflicts. In this manuscript, we extend this knowledge by investigating to what extent, if any, we also augment our external representations of the environment and its action on our bodies in response to visuo-haptic conflicts. Utilizing a mirror and a robotic brushstroking platform, we create a novel illusory paradigm that presents a visuo-haptic conflict using congruent and incongruent tactile stimuli applied to participants' fingers. Overall, we observed that participants perceived an illusory tactile sensation on their visually occluded finger when seeing a visual stimulus that was inconsistent with the actual tactile stimulus provided. We also found residual effects of the illusion after the conflict was removed. These findings highlight how our need to maintain a coherent internal representation of our body extends to our model of our environment. 

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3. A Neural Network Approach for Stochastic Optimal Control

   Li, Xingjian; Verma, Deepanshu; Ruthotto, Lars (June 2024, SIAM journal on scientific computing)

   We present a neural network approach for approximating the value function of high- dimensional stochastic control problems. Our training process simultaneously updates our value function estimate and identifies the part of the state space likely to be visited by optimal trajectories. Our approach leverages insights from optimal control theory and the fundamental relation between semi-linear parabolic partial differential equations and forward-backward stochastic differential equations. To focus the sampling on relevant states during neural network training, we use the stochastic Pontryagin maximum principle (PMP) to obtain the optimal controls for the current value function estimate. By design, our approach coincides with the method of characteristics for the non-viscous Hamilton-Jacobi-Bellman equation arising in deterministic control problems. Our training loss consists of a weighted sum of the objective functional of the control problem and penalty terms that enforce the HJB equations along the sampled trajectories. Importantly, training is unsupervised in that it does not require solutions of the control problem. Our numerical experiments highlight our scheme’s ability to identify the relevant parts of the state space and produce meaningful value estimates. Using a two-dimensional model problem, we demonstrate the importance of the stochastic PMP to inform the sampling and compare to a finite element approach. With a nonlinear control affine quadcopter example, we illustrate that our approach can handle complicated dynamics. For a 100-dimensional benchmark problem, we demonstrate that our approach improves accuracy and time-to-solution and, via a modification, we show the wider applicability of our scheme. 

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4. Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback

   https://doi.org/10.1145/3319502.3374824  

   Wang, Guan; Trimbach, Carl; Lee, Jun Ki; Ho, Mark K.; Littman, Michael L. (March 2020, Human Robot Interaction (HRI'20))

   This paper addresses the problem of training a robot to carry out temporal tasks of arbitrary complexity via evaluative human feedback that can be inaccurate. A key idea explored in our work is a kind of curriculum learning—training the robot to master simple tasks and then building up to more complex tasks. We show how a training procedure, using knowledge of the formal task representation, can decompose and train any task efficiently in the size of its representation. We further provide a set of experiments that support the claim that non-expert human trainers can decompose tasks in a way that is consistent with our theoretical results, with more than half of participants successfully training all of our experimental missions. We compared our algorithm with existing approaches and our experimental results suggest that our method outperforms alternatives, especially when feedback contains mistakes. 

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5. A Generalized Constitutive Model for Versatile MPM Simulation and Inverse Learning with Differentiable Physics

   https://doi.org/10.1145/3606925  

   Su, Haozhe; Li, Xuan; Xue, Tao; Jiang, Chenfanfu; Aanjaneya, Mridul (August 2023, Proceedings of the ACM on Computer Graphics and Interactive Techniques)

   We present a generalized constitutive model for versatile physics simulation of inviscid fluids, Newtonian viscosity, hyperelasticity, viscoplasticity, elastoplasticity, and other physical effects that arise due to a mixture of these behaviors. The key ideas behind our formulation are the design of a generalized Kirchhoff stress tensor that can describe hyperelasticity, Newtonian viscosity and inviscid fluids, and the use of pre-projection and post-correction rules for simulating material behaviors that involve plasticity, including elastoplasticity and viscoplasticity. We show how our generalized Kirchhoff stress tensor can be coupled together into a generalized constitutive model that allows the simulation of diverse material behaviors by only changing parameter values. We present several side-by-side comparisons with physics simulations for specific constitutive models to show that our generalized model produces visually similar results. More notably, our formulation allows for inverse learning of unknown material properties directly from data using differentiable physics simulations. We present several 3D simulations to highlight the robustness of our method, even with multiple different materials. To the best of our knowledge, our approach is the first to recover the knowledge of unknown material properties without making explicit assumptions about the data. 

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