Information-theoretic analysis ofdeep neural networks (DNN) has attracted recent interest due to intriguing fundamental results and new hypotheses. Applying information theory to DNNs may provide novel tools for explainable AI via estimation of information flows [1-5], as well as new ways to encourage models to extract and generalize information [1, 6-8].

Thank you for your valuable feedback, which is very helpful in improving the paper. We're encouraged by the broadly "Put this in the context of other work on computational homogenization / multi-scale finite element Our method is related to these and the boundary element method (BEM). "Limitation associated with micro-scale buckling... the coarse-grain behavior might exhibit hysteretic effects": Good "How sensitive is the outer optimization to the accuracy of the surrogate gradients?" "Do you know how the CES method scales with system size in terms of accuracy and evaluation time": In terms of "the method to solve the outer optimization over BCs to find minimum energy solutions to the composed surrogates Free DoFs are optimized to minimize total predicted energy using LBFGS. "The discuss of the surrogate and i.i.d. "Are the BCs shared when a boundary is common between two cells": Y es. We have 1 DoF for each blue point in Fig 2. "Its not clear how the HMC and PDE solver are used together": HMC is used to generate training BCs, preferring larger The PDE solver is used to compute the gradient of the pdf (which depends on E) w.r.t. the BC. Given BCs, we run the solver to determine the internal u and E. We compute dE/dBC with the Then we use this to compute the gradient of the pdf w.r.t. the BCs, needed for the leapfrog step. "does the HMC require a significant burn-in time before producing reasonable samples": No. Note: we don't truly care Per appendix, HMC took between 3 and 100 leapfrog steps per sample. The process of using the surrogates to solve the original problem can be explained in more detail. Newton method is neither the fast nor the most stable... a comparison with more sophisticated methods would be From a brief look it looks like Liu et al's method is tailored for Reviewer 5: "There is one outlier in L2 compression that was quite bad": We will discuss this in the main paper. "A comment might help the reader situate this work within the more usual (less idyllic) context of approximating This is a good suggestion: we will relate to other work in learning energies.

Previous work on modular RL has proven its ability to control morphologically different agents with a shared actuator policy. However, with the increase in the Degree of Freedom (DoF) of robots, training a morphology-generalizable modular controller becomes exponentially difficult. Motivated by the way the human central nervous system controls numerous muscles, we propose a Synergy-Oriented LeARning (SOLAR) framework that exploits the redundant nature of DoF in robot control. Actuators are grouped into synergies by an unsupervised learning method, and a synergy action is learned to control multiple actuators in synchrony. In this way, we achieve a low-rank control at the synergy level. We extensively evaluate our method on a variety of robot morphologies, and the results show its superior efficiency and generalizability, especially on robots with a large DoF like Humanoids++ and UNIMALs.

Curved objects pose a fundamental challenge for skill transfer in robotics: unlike planar surfaces, they do not admit a global reference frame. As a result, task-relevant directions such as "toward" or "along" the surface vary with position and geometry, making object-centric tasks difficult to transfer across shapes. To address this, we introduce an approach using Diffused Orientation Fields (DOF), a smooth representation of local reference frames, for transfer learning of tasks across curved objects. By expressing manipulation tasks in these smoothly varying local frames, we reduce the problem of transferring tasks across curved objects to establishing sparse keypoint correspondences. DOF is computed online from raw point cloud data using diffusion processes governed by partial differential equations, conditioned on keypoints. We evaluate DOF under geometric, topological, and localization perturbations, and demonstrate successful transfer of tasks requiring continuous physical interaction such as inspection, slicing, and peeling across varied objects. We provide our open-source codes at our website https://github.com/idiap/diffused_fields_robotics

It is also prohibitively expensive to learn a new policy from scratch for each robot hardware due to the high sample complexity of modern state-of-the-art algorithms.

Abstract-- This paper presents the SABD hand, a 16-degree-of-freedom (DoF) robotic hand that departs from purely anthropomorphic designs to achieve an expanded grasp envelope, enable manipulation poses beyond human capability, and reduce the required number of actuators. This is achieved by combining the adduction/abduction (Add/Abd) joint of digits four and five into a single joint with a large range of motion. The combined joint increases the workspace of the digits by 400% and reduces the required DoFs while retaining dexterity. Experimental results demonstrate that the combined Add/Abd joint enables the hand to grasp objects with a side distance of up to 200 mm. Reinforcement learning-based investigations show that the design enables grasping policies that are effective not only for handling larger objects but also for achieving enhanced grasp stability. In teleoperated trials, the hand successfully performed 86% of attempted grasps on suitable YCB objects, including challenging non-anthropomorphic configurations. These findings validate the design's ability to enhance grasp stability, flexibility, and dexterous manipulation without added complexity, making it well-suited for a wide range of applications. A. Motivation Robust grasping for robotic manipulation is one of the key issues preventing the usage of robots in many applications [1]. The difficulty herein can be attributed to both software [2] and hardware challenges [3]. No robotic manipulator has been able to fully match the dexterity, power-to-weight ratio, and exteroception of the human hand [4]. Commercially available solutions, such as robotic grippers [5], the Shadow Robotic Hand [6], the Allegro Hand [7] and the Leap Hand [8], tend to be expensive or overly limited in their capabilities.

Accurate parameter identification of a subject-specific human musculoskeletal model is crucial to the development of safe and reliable physically collaborative robotic systems, for instance, assistive exoskeletons. Electromyography (EMG)-based parameter identification methods have demonstrated promising performance for personalized musculoskeletal modeling, whereas their applicability is limited by the difficulty of measuring deep muscle EMGs invasively. Although several strategies have been proposed to reconstruct deep muscle EMGs or activations for parameter identification, their reliability and robustness are limited by assumptions about the deep muscle behavior. In this work, we proposed an approach to simultaneously identify the bone and superficial muscle parameters of a human arm musculoskeletal model without reconstructing the deep muscle EMGs. This is achieved by only using the least-squares solution of the deep muscle forces to calculate a loss gradient with respect to the model parameters for identifying them in a framework of differentiable optimization. The results of extensive comparative simulations manifested that our proposed method can achieve comparable estimation accuracy compared to a similar method, but with all the muscle EMGs available.

Development of dexterous manipulation hardware has primarily focused on hands and grippers. However, robotic wrists are equally critical, often playing a greater role than the end effector itself. Many conventional wrist designs fall short in human environments because they are too large or rely on rigid, high-reduction actuators that cannot support dynamic, contact-rich tasks. Some designs address these issues using backdrivable quasi-direct drive (QDD) actuators and compact form factors. However, they are often difficult to model and control due to coupled kinematics or high mechanical inertia. We present DexWrist, a robotic wrist that is designed to advance robotic manipulation in highly constrained environments, enable dynamic and contact-rich tasks, and simplify policy learning. DexWrist provides low-impedance actuation, low inertia, integrated proprioception, high speed, and a large workspace. Together, these capabilities support robust learning-based manipulation. DexWrist accelerates policy learning by: (i) enabling faster teleoperation for scalable data collection, (ii) simplifying the learned function through shorter trajectories and decoupled degrees of freedom (DOFs), (iii) providing natural backdrivability for safe contact without complex compliant controllers, and (iv) expanding the manipulation workspace in cluttered scenes. In our experiments, DexWrist improved policy success rates by 50-55% and reduced task completion times by a factor of 3-5. More details about the wrist can be found at https://dexwrist.csail.mit.edu.