Denoising is intuitively related to projection. Indeed, under the manifold hypothesis, adding random noise is approximately equivalent to orthogonal perturbation. Hence, learning to denoise is approximately learning to project. In this paper, we use this observation to interpret denoising diffusion models as approximate gradient descent applied to the Euclidean distance function. We then provide straight-forward convergence analysis of the DDIM sampler under simple assumptions on the projection error of the denoiser. Finally, we propose a new gradient-estimation sampler, generalizing DDIM using insights from our theoretical results. In as few as 5-10 function evaluations, our sampler achieves state-of-the-art FID scores on pretrained CIFAR-10 and CelebA models and can generate high quality samples on latent diffusion models.

In this article, we present a convex formulation of compliant frictional contact and a robust performant method to solve it in practice. By analytically eliminating contact constraints, we obtain an unconstrained convex problem. Our solver has proven global convergence and warm-starts effectively, enabling simulation at interactive rates. We develop compact analytical expressions of contact forces allowing us to describe our model in clear physical terms and to rigorously characterize our approximations. Moreover, this enables us not only to model point contact but also to incorporate sophisticated models of compliant contact patches. Our time stepping scheme includes the midpoint rule, which we demonstrate achieves second order accuracy even with frictional contact. We introduce a number of accuracy metrics and show that our method outperforms the existing commercial and open-source alternatives without sacrificing accuracy. Finally, we demonstrate the robust simulation of robotic manipulation tasks at interactive rates, with accurately resolved stiction and contact transitions, as required for meaningful sim-to-real transfer. Our method is implemented in the open-source robotics toolkit Drake.

We describe a whole-body dynamic walking controller implemented as a convex quadratic program. The controller solves an optimal control problem using an approximate value function derived from a simple walking model while respecting the dynamic, input, and contact constraints of the full robot dynamics. By exploiting sparsity and temporal structure in the optimization with a custom active-set algorithm, we surpass the performance of the best available off-the-shelf solvers and achieve 1kHz control rates for a 34-DOF humanoid. We describe applications to balancing and walking tasks using the simulated Atlas robot in the DARPA Virtual Robotics Challenge.

Conventionally, tendon-driven manipulators implement some force-based controller using either tension feedback or dynamic models of the actuator. The force control allows the system to maintain proper tensions on the tendons. In some cases, whether it is due to the lack of tension feedback or actuator torque control, a purely position-based controller is needed. This work compares three position controllers for tendon-driven manipulators that implement a nested actuator position controller. A new controller is introduced that achieves the best overall performance with regards to speed, accuracy, and transient behavior. To compensate for the lack of tension control, the controller nominally maintains the internal tension on the tendons through a range-space constraint on the actuator positions. These control laws are validated experimentally on the Robonaut-2 humanoid hand.

NASA and General Motors have developed the second generation Robonaut, Robonaut 2 or R2, and it is scheduled to arrive on the International Space Station in early 2011 and undergo initial testing by mid-year. This state of the art, dexterous, anthropomorphic robotic torso has significant technical improvements over its predecessor making it a far more valuable tool for astronauts. Upgrades include: increased force sensing, greater range of motion, higher bandwidth, and improved dexterity. R2's integrated mechatronic design results in a more compact and robust distributed control system with a fraction of the wiring of the original Robonaut. Modularity is prevalent throughout the hardware and software along with innovative and layered approaches for sensing and control. The most important aspects of the Robonaut philosophy are clearly present in this latest model's ability to allow comfortable human interaction and in its design to perform significant work using the same hardware and interfaces used by people. The following describes the mechanisms, integrated electronics, control strategies, and user interface that make R2 a promising addition to the Space Station and other environments where humanoid robots can assist people.

It is frequently accepted that tactile sensing must play a key role in robust manipulation and assembly. The potential exists to complement the gross shape information that vision or range sensors can provide with fine-scale information about the texture, stiffness, and shape of the object grasped. Nevertheless, no widely accepted tactile sensing technology currently exists for robot hands. Furthermore, while several proposals exist in the robotics literature regarding how to use tactile sensors to improve manipulation, there is little consensus. This paper describes the electro-mechanical design of the Robonaut 2 phalange load cell. This is a miniature load cell suitable for mounting on the phalanges of humanoid robot fingers. The important design characteristics of these load cells are the shape of the load cell spring element and the routing of small-gauge wires from the sensor onto a circuit board. The paper reports results from a stress analysis of the spring element and establishes the theoretical sensitivity of the device to loads in different directions. The paper also compares calibrated load cell data to ground truth load measurements for four different manufactured sensors. Finally, the paper analyzes the response of the load cells in the context of a flexible materials localization task.

Localization and manipulation of features such as buttons, snaps, or grommets embedded in fabrics and other flexible materials is a difficult robotics problem. Approaches that rely too much on sensing and localization that occurs before touching the material are likely to fail because the flexible material can move when the robot actually makes contact. This paper experimentally explores the possibility to use proprioceptive and load-based tactile information to localize features embedded in flexible materials during robot manipulation. In our experiments, Robonaut 2, a robot with human-like hands and arms, uses particle filtering to localize features based on proprioceptive and tactile measurements. Our main contribution is to propose a method to interact with flexible materials that reduces the state space of the interaction by forcing the material to comply in repeatable ways. Measurements are matched to a “haptic map,” which is created during a training phase, that describes expected measurements as a low-dimensional function of state. We evaluate localization performance when using proprioceptive information alone and when tactile data are also available. The two types of measurements are shown to contain complementary information. We find that the tactile measurement model is critical to localization performance and propose a series of models that offer increasingly better accuracy. Finally, this paper explores the localization approach in the context of two flexible material insertion tasks that are relevant to manufacturing applications.