Due to the complex physical properties of granular materials, research on robot learning for manipulating such materials predominantly either disregards the consideration of their physical characteristics or uses surrogate models to approximate their physical properties. Learning to manipulate granular materials based on physical information obtained through precise modeling remains an unsolved problem. In this article, we propose to address this challenge by constructing a differentiable physics-based simulator for granular materials using the Taichi programming language and developing a learning framework accelerated by demonstrations generated through gradient-based optimization on nongranular materials within our simulator, eliminating the costly data collection and model training of prior methods. Experimental results show that our method, with its flexible design, trains robust policies that are capable of executing the task of transporting granular materials in both simulated and real-world environments, beyond the capabilities of standard reinforcement learning (RL), imitation learning (IL), and prior task-specific granular manipulation methods.

Rolling contact kinematics plays a vital role in dexterous manipulation and rolling-based locomotion. Yet, in practical applications, the environments and objects involved are often captured as discrete point clouds, creating substantial difficulties for traditional motion control and planning frameworks that rely on continuous surface representations. In this work, we propose a differential geometry-based framework that models point cloud data for continuous rolling contact using locally parameterized representations. Our approach leverages skeletonization to define a rotational reference structure for rolling interactions and applies a Fourier-based curve fitting technique to extract and represent meaningful controllable local geometric structure. We further introduce a novel 2D manifold coordinate system tailored to arbitrary surface curves, enabling local parameterization of complex shapes. The governing kinematic equations for rolling contact are then derived, and we demonstrate the effectiveness of our method through simulations on various object examples.

The ongoing COVID-19 pandemic has enforced governments across the world to impose social restrictions on the movement of people and confined them to their homes to avoid the spread of the disease. This not only forbids them from leaving their homes but also greatly reduces their physical activities. This situation has brought attention to virtual technologies such as virtual tours or telepresence robots. While these technologies allow people to remotely participate in activities, it does not address the problem of reduction in physical activities due to the pandemic. In this paper, we propose a telepresence robotic system driven by the user's gait to provide an immersive virtual walking experience in remote locations. To this end, we developed a control interface consisting of an automated treadmill that adjusts its speed to the user's pace automatically. This interface is used to control an avatar robot that sends a 360-degree live image back to the user for visual feedback. We conducted an evaluation experiment to compare the experience using the proposed system in two different conditions to that of regular walking. The results indicated that the proposed system gives an immersive and realistic virtual walking experience while demanding physical effort from the user.

Certain wheeled mobile robots e.g., electric wheelchairs, can operate through indirect joystick controls from users. Correct steering angle becomes essential when the user should determine the vehicle direction and velocity, in particular for differential wheeled vehicles since the vehicle velocity and direction are controlled with only two actuating wheels. This problem gets more challenging when complex curves should be realized by the user. A novel assistive controller with safety constraints is needed to address these problems. Also, the classic control methods mostly require the desired states beforehand which completely contradicts human's spontaneous decisions on the desired location to go. In this work, we develop a novel assistive control strategy based on differential geometry relying on only joystick inputs and vehicle states where the controller does not require any desired states. We begin with explaining the vehicle kinematics and our designed Darboux frame kinematics on a contact point of a virtual wheel and plane. Next, the geometric controller using the Darboux frame kinematics is designed for having smooth trajectories under certain safety constraints. We experiment our approach with different participants and evaluate its performance in various routes.

Quantifying the safety of the human body ori-entation is an important issue in human-robot interaction. Knowing the changing physical constraints on human motion can improve inspection of safe human motions and bring essential information about stability and normality of human body orientations with real-time risk assessment. Also, this information can be used in cooperative robots and monitoring systems to evaluate and interact in the environment more freely. Furthermore, the workspace area can be more deterministic with the known physical characteristics of safety. Based on this motivation, we propose a novel predictive safety model (PSM) that relies on the information of an inertial measurement unit on the human chest. The PSM encompasses a 3-Dofs spring-damper pendulum model that predicts human motion based on a safe motion dataset. The estimated safe orientation of humans is obtained by integrating a safety dataset and an elastic spring-damper model in a way that the proposed approach can realize complex motions at different safety levels. We did experiments in a real-world scenario to verify our novel proposed model. This novel approach can be used in different guidance/assistive robots and health monitoring systems to support and evaluate the human condition, particularly elders.

Motion control of underactuated systems through the inverse dynamics contains configuration singularities. These limitations in configuration space mainly stem from the inertial coupling that passive joints/bodies create. In this study, we present a model that is free from singularity while the trajectory of the rotating mass has a small-amplitude sine wave around its circle. First, we derive the modified non-linear dynamics for a rolling system. Also, the singularity regions for this underactuated system is demonstrated. Then, the wave parameters are designed under certain conditions to remove the coupling singularities. We obtain these conditions from the positive definiteness of the inertia matrix in the inverse dynamics. Finally, the simulation results are confirmed by using a prescribed Beta function on the specified states of the rolling carrier. Because our algebraic method is integrated into the non-linear dynamics, the proposed solution has a great potential to be extended to the Lagrangian mechanics with multiple degrees-of-freedom.