Abstract-- Robotic exploration in extreme environments--such as caves, oceans, and planetary surfaces--poses significant challenges, particularly in locomotion across diverse terrains. Conventional wheeled or legged robots often struggle in these contexts due to surface variability. This paper presents ARCSnake V2, an amphibious, screw-propelled, snake-like robot designed for teleoperated or autonomous locomotion across land, granular media, and aquatic environments. ARCSnake V2 combines the high mobility of hyper-redundant snake robots with the terrain versatility of Archimedean screw propulsion. Key contributions include a water-sealed mechanical design with serially linked screw and joint actuation, an integrated buoyancy control system, and teleoperation via a kinematically-matched handheld controller . The robot's design and control architecture enable multiple locomotion modes--screwing, wheeling, and sidewinding--with smooth transitions between them. Robotic exploration in extreme environments, such as caves, oceans and planetary surfaces, poses significant challenges for the diverse set of terrains [1].

Tensegrity robots, composed of rigid struts and elastic tendons, provide impact resistance, low mass, and adaptability to unstructured terrain. Their compliance and complex, coupled dynamics, however, present modeling and control challenges, hindering path planning and obstacle avoidance. This paper presents a complete, open-source, and reproducible system that enables navigation for a 3-bar tensegrity robot. The system comprises: (i) an inexpensive, open-source hardware design, and (ii) an integrated, open-source software stack for physics-based modeling, system identification, state estimation, path planning, and control. All hardware and software are publicly available at https://sites.google.com/view/tensegrity-navigation/. The proposed system tracks the robot's pose and executes collision-free paths to a specified goal among known obstacle locations. System robustness is demonstrated through experiments involving unmodeled environmental challenges, including a vertical drop, an incline, and granular media, culminating in an outdoor field demonstration. To validate reproducibility, experiments were conducted using robot instances at two different laboratories. This work provides the robotics community with a complete navigation system for a compliant, impact-resistant, and shape-morphing robot. This system is intended to serve as a springboard for advancing the navigation capabilities of other unconventional robotic platforms.

Abstract-- Reliable autonomous navigation across the unstructured terrains of distant planetary surfaces is a critical enabler for future space exploration. However, the deployment of learning-based controllers is hindered by the inherent sim-to-real gap, particularly for the complex dynamics of wheel interactions with granular media. This work presents a complete sim-to-real framework for developing and validating robust control policies for dynamic waypoint tracking on such challenging surfaces. We leverage massively parallel simulation to train reinforcement learning agents across a vast distribution of procedurally generated environments with randomized physics. These policies are then transferred zero-shot to a physical wheeled rover operating in a lunar-analogue facility. Our experiments systematically compare multiple reinforcement learning algorithms and action smoothing filters to identify the most effective combinations for real-world deployment. Crucially, we provide strong empirical evidence that agents trained with procedural diversity achieve superior zero-shot performance compared to those trained on static scenarios. We also analyze the trade-offs of fine-tuning with high-fidelity particle physics, which offers minor gains in low-speed precision at a significant computational cost. T ogether, these contributions establish a validated workflow for creating reliable learning-based navigation systems, marking a substantial step towards deploying autonomous robots in the final frontier .

Elongate animals and robots use undulatory body waves to locomote through diverse environments. Geometric mechanics provides a framework to model and optimize such systems in highly damped environments, connecting a prescribed shape change pattern (gait) with locomotion displacement. However, the practical applicability of controlling compliant physical robots remains to be demonstrated. In this work, we develop a framework based on geometric mechanics to predict locomotor performance and search for optimal swimming strategies of compliant swimmers. We introduce a compliant extension of Purcell's three-link swimmer by incorporating series-connected springs at the joints. Body dynamics are derived using resistive force theory. Geometric mechanics is incorporated into movement prediction and into an optimization framework that identifies strategies for controlling compliant swimmers to achieve maximal displacement. We validate our framework on a physical cable-driven three-link limbless robot and demonstrate accurate prediction and optimization of locomotor performance under varied programmed, state-dependent compliance in a granular medium. Our results establish a systematic, physics-based approach for modeling and controlling compliant swimming locomotion, highlighting compliance as a design feature that can be exploited for robust movement in both homogeneous and heterogeneous environments.

Abstract-- Autonomous manipulation of granular media, such as sand, is crucial for applications in construction, excavation, and additive manufacturing. However, shaping granular materials presents unique challenges due to their high-dimensional configuration space and complex dynamics, where traditional rule-based approaches struggle without extensive engineering efforts. Reinforcement learning (RL) offers a promising alternative by enabling agents to learn adaptive manipulation strategies through trial and error . In this work, we present an RL framework that enables a robotic arm with a cubic end-effector and a stereo camera to shape granular media into desired target structures. We show the importance of compact observations and concise reward formulations for the large configuration space, validating our design choices with an ablation study. Our results demonstrate the effectiveness of the proposed approach for the training of visual policies that manipulate granular media including their real-world deployment, significantly outperforming two baseline approaches in terms of target shape accuracy. The ability to manipulate granular media such as sand has many applications in robotics, ranging from construction and excavation [1]-[8] to additive manufacturing [9]. Unlike the manipulation of rigid bodies, the shaping of granular media is accompanied by unique challenges due to their particle nature.

Simulating granular media, using Discrete Element Method is a computationally intensive task. This is especially true during initialization phase, which dominates total simulation time because of large displacements involved and associated kinetic energy. We overcome this bottleneck with a novel generative pipeline based on 3D diffusion models that directly synthesizes arbitrarily large granular assemblies in their final and physically realistic configurations. The approach frames the problem as a 3D generative modeling task, consisting of a two-stage pipeline. First a diffusion model is trained to generate independent 3D voxel grids representing granular media. Second, a 3D inpainting model, adapted from 2D inpainting techniques using masked inputs, stitches these grids together seamlessly, enabling synthesis of large samples with physically realistic structure. The inpainting model explores several masking strategies for the inputs to the underlying UNets by training the network to infer missing portions of voxel grids from a concatenation of noised tensors, masks, and masked tensors as input channels. The model also adapts a 2D repainting technique of re-injecting noise scheduler output with ground truth to provide a strong guidance to the 3D model. This along with weighted losses ensures long-term coherence over generation of masked regions. Both models are trained on the same binarized 3D occupancy grids extracted from small-scale DEM simulations, achieving linear scaling of computational time with respect to sample size. Quantitatively, a 1.2 m long ballasted rail track synthesis equivalent to a 3-hour DEM simulation, was completed under 20 seconds. The generated voxel grids can also be post-processed to extract grain geometries for DEM-compatibility as well, enabling physically coherent, real-time, scalable granular media synthesis for industrial applications.

The proximity perception of objects in granular materials is significant, especially for applications like minesweeping. However, due to particles' opacity and complex properties, existing proximity sensors suffer from high costs from sophisticated hardware and high user-cost from unintuitive results. In this paper, we propose a simple yet effective proximity sensing system for underground stuff based on the haptic feedback of the sensor-granules interaction. We study and employ the unique characteristic of particles -- failure wedge zone, and combine the machine learning method -- Gaussian process regression, to identify the force signal changes induced by the proximity of objects, so as to achieve near-field perception. Furthermore, we design a novel trajectory to control the probe searching in granules for a wide range of perception. Also, our proximity sensing system can adaptively determine optimal parameters for robustness operation in different particles. Experiments demonstrate our system can perceive underground objects over 0.5 to 7 cm in advance among various materials.

While tangible user interface has shown its power in naturally interacting with rigid or soft objects, users cannot conveniently use different types of granular materials as the interaction media. We introduce DipMe as a smart device to recognize the types of granular media in real time, which can be used to connect the granular materials in the physical world with various virtual content. Other than vision-based solutions, we propose a dip operation of our device and exploit the haptic signals to recognize different types of granular materials. With modern machine learning tools, we find the haptic signals from different granular media are distinguishable by DipMe. With the online granular object recognition, we build several tangible interactive applications, demonstrating the effects of DipMe in perceiving granular materials and its potential in developing a tangible user interface with granular objects as the new media.

Recent advancements in learned 3D representations have enabled significant progress in solving complex robotic manipulation tasks, particularly for rigid-body objects. However, manipulating granular materials such as beans, nuts, and rice, remains challenging due to the intricate physics of particle interactions, high-dimensional and partially observable state, inability to visually track individual particles in a pile, and the computational demands of accurate dynamics prediction. Current deep latent dynamics models often struggle to generalize in granular material manipulation due to a lack of inductive biases. In this work, we propose a novel approach that learns a visual dynamics model over Gaussian splatting representations of scenes and leverages this model for manipulating granular media via Model-Predictive Control. Our method enables efficient optimization for complex manipulation tasks on piles of granular media. We evaluate our approach in both simulated and real-world settings, demonstrating its ability to solve unseen planning tasks and generalize to new environments in a zero-shot transfer. We also show significant prediction and manipulation performance improvements compared to existing granular media manipulation methods.

Manipulation tasks often require a high degree of dexterity, typically necessitating grippers with multiple degrees of freedom (DoF). While a robotic hand equipped with multiple fingers can execute precise and intricate manipulation tasks, the inherent redundancy stemming from its extensive DoF often adds unnecessary complexity. In this paper, we introduce the design of a tactile sensor-equipped gripper with two fingers and five DoF. We present a novel design integrating a GelSight tactile sensor, enhancing sensing capabilities and enabling finer control during specific manipulation tasks. To evaluate the gripper's performance, we conduct experiments involving two challenging tasks: 1) retrieving, singularizing, and classification of various objects embedded in granular media, and 2) executing scooping manipulations of credit cards in confined environments to achieve precise insertion. Our results demonstrate the efficiency of the proposed approach, with a high success rate for singulation and classification tasks, particularly for spherical objects at high as 94.3%, and a 100% success rate for scooping and inserting credit cards.