We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal attributes: mechanical properties, external forces, and rest-shape geometry. Existing single-view 3D reconstruction methods often overlook this underlying composition, presuming rigidity or neglecting external forces. Consequently, the reconstructed objects fail to withstand real-world physical forces, resulting in instability or undesirable deformation -- diverging from their intended designs as depicted in the image.

Electronic devices are essential for robots but limit their usable environments. To overcome this, methods excluding electronics from the operating environment while retaining advanced electronic control and actuation have been explored. These include the remote hydraulic drive of electronics-free mobile robots, which offer high reachability, and long wire-driven robot arms with motors consolidated at the base, which offer high environmental resistance. To combine the advantages of both, this study proposes a new system, "Remote Wire Drive." As a proof-of-concept, we designed and developed the Remote Wire-Driven robot "REWW-ARM", which consists of the following components: 1) a novel power transmission mechanism, the "Remote Wire Transmission Mechanism" (RWTM), the key technology of the Remote Wire Drive; 2) an electronics-free distal mobile robot driven by it; and 3) a motor-unit that generates power and provides electronic closed-loop control based on state estimation via the RWTM. In this study, we evaluated the mechanical and control performance of REWW-ARM through several experiments, demonstrating its capability for locomotion, posture control, and object manipulation both on land and underwater. This suggests the potential for applying the Remote Wire-Driven system to various types of robots, thereby expanding their operational range.

Virtual reality (VR) teleoperation has emerged as a promising approach for controlling humanoid robots in complex manipulation tasks. However, traditional tele-operation systems rely on inverse kinematics (IK) solvers and hand-tuned PD controllers, which struggle to handle external forces, adapt to different users, and produce natural motions under dynamic conditions. In this work, we propose a learning-based neural teleoperation framework that replaces the conventional IK+PD pipeline with learned policies trained via reinforcement learning. Our approach learns to directly map VR controller inputs to robot joint commands while implicitly handling force disturbances, producing smooth trajectories, and adapting to user preferences. We train our policies in simulation using demonstrations collected from IK-based teleoperation as initialization, then fine-tune them with force randomization and trajectory smoothness rewards. Experiments on the Unitree G1 humanoid robot demonstrate that our learned policies achieve 34% lower tracking error, 45% smoother motions, and superior force adaptation compared to the IK baseline, while maintaining real-time performance (50Hz control frequency). We validate our approach on manipulation tasks including object pick-and-place, door opening, and bimanual coordination. These results suggest that learning-based approaches can significantly improve the naturalness and robustness of humanoid teleoperation systems.

This well-studied problem has plenty of proposed solutions (known as Large Neighborhood Search methods), though.

The last term in the loss function of COMET does not need the information from the dataset. I) is the Gaussian noise with standard deviation σ = 0 . This section contains the experiment details for cases tested in section 4. In section 4, there are 6 simple experiments performed to demonstrate the capability of COMET: (1) mass-spring, (2) 2D The description for each simulation case can be found below. This is the simplest case to test COMET's capability where Case 3: 2D damped pendulum. The training data were generated in a similar way as the previous case.

The beauty of physics is that there is usually a conserved quantity in an always-changing system, known as the constant of motion.

We train humanoid policies that compliantly respond to external forces while tracking a reference motion. The desired force-displacement relationship is modulated by a'stiffness' input at deployment time, and a single policy learns to realize a wide range of stiffnesses. In the images, the reference motion is visualized in blue, and the approximate external force on the robot is illustrated by the red arrows. Abstract-- We introduce SoftMimic, a framework for learning compliant whole-body control policies for humanoid robots from example motions. Imitating human motions with reinforcement learning allows humanoids to quickly learn new skills, but existing methods incentivize stiff control that aggressively corrects deviations from a reference motion, leading to brittle and unsafe behavior when the robot encounters unexpected contacts. In contrast, SoftMimic enables robots to respond compliantly to external forces while maintaining balance and posture. Our approach leverages an inverse kinematics solver to generate an augmented dataset of feasible compliant motions, which we use to train a reinforcement learning policy. By rewarding the policy for matching compliant responses rather than rigidly tracking the reference motion, SoftMimic learns to absorb disturbances and generalize to varied tasks from a single motion clip. I. INTRODUCTION A major goal in humanoid robotics is to build agents capable of performing a vast range of tasks humans execute in everyday environments. A promising avenue towards this goal is to leverage large-scale human motion capture data, enabling robots to learn human-like behaviors through imitation [1]. All authors are with the Improbable AI Lab, Massachusetts Institute of Technology, USA.

Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been extensively developed for robotic arms, enabling compliant human-humanoid collaboration remains largely unexplored due to humanoids' complex whole-body dynamics. In this paper, we propose a proprioception-only reinforcement learning approach, COLA, that combines leader and follower behaviors within a single policy. The model is trained in a closed-loop environment with dynamic object interactions to predict object motion patterns and human intentions implicitly, enabling compliant collaboration to maintain load balance through coordinated trajectory planning. We evaluate our approach through comprehensive simulator and real-world experiments on collaborative carrying tasks, demonstrating the effectiveness, generalization, and robustness of our model across various terrains and objects. Simulation experiments demonstrate that our model reduces human effort by 24.7%. compared to baseline approaches while maintaining object stability. Real-world experiments validate robust collaborative carrying across different object types (boxes, desks, stretchers, etc.) and movement patterns (straight-line, turning, slope climbing). Human user studies with 23 participants confirm an average improvement of 27.4% compared to baseline models. Our method enables compliant human-humanoid collaborative carrying without requiring external sensors or complex interaction models, offering a practical solution for real-world deployment.

Abstract-- Humanoid locomotion requires not only accurate command tracking for navigation but also compliant responses to external forces during human interaction. Despite significant progress, existing RL approaches mainly emphasize robustness, yielding policies that resist external forces but lack compliance-particularly challenging for inherently unstable humanoids. In this work, we address this by formulating humanoid locomotion as a multi-objective optimization problem that balances command tracking and external force compliance. We introduce a preference-conditioned multi-objective RL (MORL) framework that integrates rigid command following and compliant behaviors within a single omnidirectional locomotion policy. External forces are modeled via velocity-resistance factor for consistent reward design, and training leverages an encoder-decoder structure that infers task-relevant privileged features from deployable observations. Experimental results indicate that our framework not only improves adaptability and convergence over standard pipelines, but also realizes deployable preference-conditioned humanoid locomotion. Video can be found in the link.