Zeynep Temel

Victoria A. Webster-Wood, Nicholas Gravish, Amir Alavi et al.

This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself. This is in contrast to computational intelligence, wherein the intelligence functions occur through electrical signaling and computer code. The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF. The workshop was structured around active small and large group discussions in groups of 4-5 and 9-10 with the goal of addressing topical questions on MI. Working groups entered notes into shared presentation slides for each discussion session and presented their outcomes in a final presentation on the last day. Here we summarize the overall outcomes of the workshop.

Anya Zorin, Zilin Si, Myungsun Park et al.

Vision-based learning from demonstrations has achieved remarkable success in enabling robots to perform manipulation tasks and high-level semantic reasoning, yet it remains insufficient for complex, contact-rich manipulation. While there is broad agreement that tactile sensing improves manipulation, there is no empirical guidance on which tactile sensors are best suited for which manipulation tasks. In this paper, we provide a systematic, task-driven evaluation of tactile sensors for robot manipulation and propose a framework for selecting and evaluating sensors based on manipulation policy performance. Separate manipulation policies are trained for tactile sensors of four distinct modalities: visual, acoustic, magnetic, and resistive, across three tasks: pick-and-place with unknown mass, object reorientation, and plug insertion. For each task, an analysis of how sensor properties such as spatial resolution, shear sensing, and tactile representation, and the inherent material friction affect task performances is done. Rather than tactile sensing being universally beneficial in the same way, our results show that the usefulness of tactile information depends strongly on sensor modality, material properties, and the specific manipulation tasks. All of the tactile sensors, code, data, and hardware setup will be publicly available on the project website.

Shashwat Singh, Sheri Zhang, Spencer Matonis et al.

Mudskippers are unique amphibious fish capable of locomotion in diverse environments, including terrestrial surfaces, aquatic habitats, and highly viscous substrates such as mud. This versatile locomotion is largely enabled by their powerful tail, which stores and rapidly releases energy to produce impulsive jumps. Inspired by this biological mechanism, we present the design and development of a multi-terrain centimeter-scale skipping and crawling robot. The robot is predominantly 3D printed and features onboard sensing, computation, and power. It is equipped with two side fins for crawling, each integrated with a hall effect sensor for gait control, while a rotary springtail driven by a 10mm planetary gear motor enables continuous impulsive skipping across a range of substrates to achieve multi-terrain locomotion. We modeled and experimentally characterized the tail, identifying an optimal length of 25mm that maximizes the mean propulsive force (4N, peaks up to 6N) for forward motion. In addition, we evaluated skipping on substrates where fin based crawling alone fails, and varied the moisture content of uniform sand and bentonite clay powder to compare skipping with crawling. Skipping consistently produced higher mean velocities than crawling, particularly on viscous and granular media. Finally, outdoor tests on grass, loose sand, and hard ground confirmed that combining skipping on entangling and granular terrain with crawling on firm ground extends the operational range of the robot in real-world environments.

Tianhui Han, Shashwat Singh, Sarvesh Patil et al.

Origami-inspired mechanisms can transform flat sheets into functional three-dimensional dynamic structures that are lightweight, compact, and capable of complex motion. These properties make origami increasingly valuable in robotic and deployable systems. However, accurately simulating their folding behavior and interactions with the environment remains challenging. To address this, we present a design framework for origami mechanism simulation that utilizes MuJoCo's deformable-body capabilities. In our approach, origami sheets are represented as graphs of interconnected deformable elements with user-specified constraints such as creases and actuation, defined through an intuitive graphical user interface (GUI). This framework allows users to generate physically consistent simulations that capture both the geometric structure of origami mechanisms and their interactions with external objects and surfaces. We demonstrate our method's utility through a case study on an origami catapult, where design parameters are optimized in simulation using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and validated experimentally on physical prototypes. The optimized structure achieves improved throwing performance, illustrating how our system enables rapid, simulation-driven origami design, optimization, and analysis.

Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal et al.

Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning. This work introduces Off-policy Generative Policy Optimization (OGPO), a sample-efficient algorithm for finetuning GCPs that maintains off-policy critic networks to maximize data reuse and propagate policy gradients through the full generative process of the policy via a modified PPO objective, using critics as the terminal reward. OGPO achieves state-of-the-art performance on manipulation tasks spanning multi-task settings, high-precision insertion, and dexterous control. To our knowledge, it is also the only method that can fine-tune poorly-initialized behavior cloning policies to near full task-success with no expert data in the online replay buffer, and does so with few task-specific hyperparameter tuning. Through extensive empirical investigations, we demonstrate the OGPO drastically outperforms methods alternatives on policy steering and learning residual corrections, and identify the key mechanisms behind its performance. We further introduce practical stabilizers, including success-buffer regularization, conservative advantages, $χ^2$ regularization, and Q-variance reduction, to mitigate critic over-exploitation across state- and pixel-based settings. Beyond proposing OGPO, we conduct a systematic empirical study of GCP finetuning, identifying the stabilizing mechanisms and failure modes that govern successful off-policy full-policy improvement.

Sha Yi, Zeynep Temel, Katia Sycara

In this paper, we present a heterogeneous robot swarm system that can physically couple with each other to form functional structures and dynamically decouple to perform individual tasks. The connection between robots can be formed with a passive coupling mechanism, ensuring minimum energy consumption during coupling and decoupling behavior. The heterogeneity of the system enables the robots to perform structural enhancement configurations based on specific environmental requirements. We propose a connection-pair oriented configuration control algorithm to form different assemblies. We show experiments of up to nine robots performing the coupling, gap-crossing, and decoupling behaviors.

Fukang Liu, Vaidehi Patil, Zackory Erickson et al.

Robotic bathing assistance has long been considered an important and practical task in healthcare. Yet, achieving flexible and efficient cleaning tasks on the human body is challenging, since washing the body involves direct human-robot physical contact and simple, safe, and effective devices are needed for bathing and hygiene. In this paper, we present a meso-scale wearable robot that can locomote along the human body to provide bathing and skin care assistance. We evaluated the cleaning performance of the robot system under different scenarios. The experiments on the pipe show that the robot can achieve cleaning percentage over 92% with two types of stretchable fabrics. The robot removed most of the debris with average values of 94% on a human arm and 93% on a manikin torso. The results demonstrate that the robot exhibits high performance in cleaning tasks.

Sha Yi, Zeynep Temel, Katia Sycara

Robot swarms have been shown to improve the ability of individual robots by inter-robot collaboration. In this paper, we present the PuzzleBots - a low-cost robotic swarm system where robots can physically couple with each other to form functional structures with minimum energy consumption while maintaining individual mobility to navigate within the environment. Each robot has knobs and holes along the sides of its body so that the robots can couple by inserting the knobs into the holes. We present the characterization of knob design and the result of gap-crossing behavior with up to nine robots. We show with hardware experiments that the robots are able to couple with each other to cross gaps and decouple to perform individual tasks. We anticipate the PuzzleBots will be useful in unstructured environments as individuals and coupled systems in real-world applications.