Inclusion in Assistive Haircare Robotics: Practical and Ethical Considerations in Hair Manipulation

Robot haircare systems could provide a controlled and personalized environment that is respectful of an individual's sensitivities and may offer a comfortable experience. We argue that because of hair and hairstyles' often unique importance in defining and expressing an individual's identity, we should approach the development of assistive robot haircare systems carefully while considering various practical and ethical concerns and risks. In this work, we specifically list and discuss the consideration of hair type, expression of the individual's preferred identity, cost accessibility of the system, culturally-aware robot strategies, and the associated societal risks. Finally, we discuss the planned studies that will allow us to better understand and address the concerns and considerations we outlined in this work through interactions with both haircare experts and end-users. Through these practical and ethical considerations, this work seeks to systematically organize and provide guidance for the development of inclusive and ethical robot haircare systems.

BOMP: Bin-Optimized Motion Planning

In logistics, the ability to quickly compute and execute pick-and-place motions from bins is critical to increasing productivity. We present Bin-Optimized Motion Planning (BOMP), a motion planning framework that plans arm motions for a six-axis industrial robot with a long-nosed suction tool to remove boxes from deep bins. BOMP considers robot arm kinematics, actuation limits, the dimensions of a grasped box, and a varying height map of a bin environment to rapidly generate time-optimized, jerk-limited, and collision-free trajectories. The optimization is warm-started using a deep neural network trained offline in simulation with 25,000 scenes and corresponding trajectories. Experiments with 96 simulated and 15 physical environments suggest that BOMP generates collision-free trajectories that are up to 58 % faster than baseline sampling-based planners and up to 36 % faster than an industry-standard Up-Over-Down algorithm, which has an extremely low 15 % success rate in this context. BOMP also generates jerk-limited trajectories while baselines do not. Website: https://sites.google.com/berkeley.edu/bomp.

Automating Robot Failure Recovery Using Vision-Language Models With Optimized Prompts

Current robot autonomy struggles to operate beyond the assumed Operational Design Domain (ODD), the specific set of conditions and environments in which the system is designed to function, while the real-world is rife with uncertainties that may lead to failures. Automating recovery remains a significant challenge. Traditional methods often rely on human intervention to manually address failures or require exhaustive enumeration of failure cases and the design of specific recovery policies for each scenario, both of which are labor-intensive. Foundational Vision-Language Models (VLMs), which demonstrate remarkable common-sense generalization and reasoning capabilities, have broader, potentially unbounded ODDs. However, limitations in spatial reasoning continue to be a common challenge for many VLMs when applied to robot control and motion-level error recovery. In this paper, we investigate how optimizing visual and text prompts can enhance the spatial reasoning of VLMs, enabling them to function effectively as black-box controllers for both motion-level position correction and task-level recovery from unknown failures. Specifically, the optimizations include identifying key visual elements in visual prompts, highlighting these elements in text prompts for querying, and decomposing the reasoning process for failure detection and control generation. In experiments, prompt optimizations significantly outperform pre-trained Vision-Language-Action Models in correcting motion-level position errors and improve accuracy by 65.78% compared to VLMs with unoptimized prompts. Additionally, for task-level failures, optimized prompts enhanced the success rate by 5.8%, 5.8%, and 7.5% in VLMs' abilities to detect failures, analyze issues, and generate recovery plans, respectively, across a wide range of unknown errors in Lego assembly.

RoPotter: Toward Robotic Pottery and Deformable Object Manipulation with Structural Priors

Humans are capable of continuously manipulating a wide variety of deformable objects into complex shapes. This is made possible by our intuitive understanding of material properties and mechanics of the object, for reasoning about object states even when visual perception is occluded. These capabilities allow us to perform diverse tasks ranging from cooking with dough to expressing ourselves with pottery-making. However, developing robotic systems to robustly perform similar tasks remains challenging, as current methods struggle to effectively model volumetric deformable objects and reason about the complex behavior they typically exhibit. To study the robotic systems and algorithms capable of deforming volumetric objects, we introduce a novel robotics task of continuously deforming clay on a pottery wheel. We propose a pipeline for perception and pottery skill-learning, called RoPotter, wherein we demonstrate that structural priors specific to the task of pottery-making can be exploited to simplify the pottery skill-learning process. Namely, we can project the cross-section of the clay to a plane to represent the state of the clay, reducing dimensionality. We also demonstrate a mesh-based method of occluded clay state recovery, toward robotic agents capable of continuously deforming clay. Our experiments show that by using the reduced representation with structural priors based on the deformation behaviors of the clay, RoPotter can perform the long-horizon pottery task with 44.4% lower final shape error compared to the state-of-the-art baselines.

KOROL: Learning Visualizable Object Feature with Koopman Operator Rollout for Manipulation

Learning dexterous manipulation skills presents significant challenges due to complex nonlinear dynamics that underlie the interactions between objects and multi-fingered hands. Koopman operators have emerged as a robust method for modeling such nonlinear dynamics within a linear framework. However, current methods rely on runtime access to ground-truth (GT) object states, making them unsuitable for vision-based practical applications. Unlike image-to-action policies that implicitly learn visual features for control, we use a dynamics model, specifically the Koopman operator, to learn visually interpretable object features critical for robotic manipulation within a scene. We construct a Koopman operator using object features predicted by a feature extractor and utilize it to auto-regressively advance system states. We train the feature extractor to embed scene information into object features, thereby enabling the accurate propagation of robot trajectories. We evaluate our approach on simulated and real-world robot tasks, with results showing that it outperformed the model-based imitation learning NDP by 1.08$\times$ and the image-to-action Diffusion Policy by 1.16$\times$. The results suggest that our method maintains task success rates with learned features and extends applicability to real-world manipulation without GT object states.

Self-Supervised Learning of Dynamic Planar Manipulation of Free-End Cables

Dynamic manipulation of free-end cables has applications for cable management in homes, warehouses and manufacturing plants. We present a supervised learning approach for dynamic manipulation of free-end cables, focusing on the problem of getting the cable endpoint to a designated target position, which may lie outside the reachable workspace of the robot end effector. We present a simulator, tune it to closely match experiments with physical cables, and then collect training data for learning dynamic cable manipulation. We evaluate with 3 cables and a physical UR5 robot. Results over 32x5 trials on 3 cables suggest that a physical UR5 robot can attain a median error distance ranging from 22% to 35% of the cable length among cables, outperforming an analytic baseline by 21% and a Gaussian Process baseline by 7% with lower interquartile range (IQR).

Residual-NeRF: Learning Residual NeRFs for Transparent Object Manipulation

Transparent objects are ubiquitous in industry, pharmaceuticals, and households. Grasping and manipulating these objects is a significant challenge for robots. Existing methods have difficulty reconstructing complete depth maps for challenging transparent objects, leaving holes in the depth reconstruction. Recent work has shown neural radiance fields (NeRFs) work well for depth perception in scenes with transparent objects, and these depth maps can be used to grasp transparent objects with high accuracy. NeRF-based depth reconstruction can still struggle with especially challenging transparent objects and lighting conditions. In this work, we propose Residual-NeRF, a method to improve depth perception and training speed for transparent objects. Robots often operate in the same area, such as a kitchen. By first learning a background NeRF of the scene without transparent objects to be manipulated, we reduce the ambiguity faced by learning the changes with the new object. We propose training two additional networks: a residual NeRF learns to infer residual RGB values and densities, and a Mixnet learns how to combine background and residual NeRFs. We contribute synthetic and real experiments that suggest Residual-NeRF improves depth perception of transparent objects. The results on synthetic data suggest Residual-NeRF outperforms the baselines with a 46.1% lower RMSE and a 29.5% lower MAE. Real-world qualitative experiments suggest Residual-NeRF leads to more robust depth maps with less noise and fewer holes. Website: https://residual-nerf.github.io

POE: Acoustic Soft Robotic Proprioception for Omnidirectional End-effectors

Soft robotic shape estimation and proprioception are challenging because of soft robot's complex deformation behaviors and infinite degrees of freedom. A soft robot's continuously deforming body makes it difficult to integrate rigid sensors and to reliably estimate its shape. In this work, we present Proprioceptive Omnidirectional End-effector (POE), which has six embedded microphones across the tendon-driven soft robot's surface. We first introduce novel applications of previously proposed 3D reconstruction methods to acoustic signals from the microphones for soft robot shape proprioception. To improve the proprioception pipeline's training efficiency and model prediction consistency, we present POE-M. POE-M first predicts key point positions from the acoustic signal observations with the embedded microphone array. Then we utilize an energy-minimization method to reconstruct a physically admissible high-resolution mesh of POE given the estimated key points. We evaluate the mesh reconstruction module with simulated data and the full POE-M pipeline with real-world experiments. We demonstrate that POE-M's explicit guidance of the key points during the mesh reconstruction process provides robustness and stability to the pipeline with ablation studies. POE-M reduced the maximum Chamfer distance error by 23.10 % compared to the state-of-the-art end-to-end soft robot proprioception models and achieved 4.91 mm average Chamfer distance error during evaluation.

MD-Splatting: Learning Metric Deformation from 4D Gaussians in Highly Deformable Scenes

Accurate 3D tracking in highly deformable scenes with occlusions and shadows can facilitate new applications in robotics, augmented reality, and generative AI. However, tracking under these conditions is extremely challenging due to the ambiguity that arises with large deformations, shadows, and occlusions. We introduce MD-Splatting, an approach for simultaneous 3D tracking and novel view synthesis, using video captures of a dynamic scene from various camera poses. MD-Splatting builds on recent advances in Gaussian splatting, a method that learns the properties of a large number of Gaussians for state-of-the-art and fast novel view synthesis. MD-Splatting learns a deformation function to project a set of Gaussians with non-metric, thus canonical, properties into metric space. The deformation function uses a neural-voxel encoding and a multilayer perceptron (MLP) to infer Gaussian position, rotation, and a shadow scalar. We enforce physics-inspired regularization terms based on local rigidity, conservation of momentum, and isometry, which leads to trajectories with smaller trajectory errors. MD-Splatting achieves high-quality 3D tracking on highly deformable scenes with shadows and occlusions. Compared to state-of-the-art, we improve 3D tracking by an average of 23.9 %, while simultaneously achieving high-quality novel view synthesis. With sufficient texture such as in scene 6, MD-Splatting achieves a median tracking error of 3.39 mm on a cloth of 1 x 1 meters in size. Project website: https://md-splatting.github.io/.

FogROS2-Sky: Optimizing Latency and Cost for Multi-Cloud Robot Applications

This paper studies the cost-performance tradeoffs in cloud robotics with heterogeneous cloud service providers, which have complex pricing models and varying application requirements. We present FogROS2-Sky, a cost-efficient open source robotics platform that offloads unmodified ROS2 applications to multiple cloud providers and enables fine-grained cost analysis for ROS2 applications' communication with multiple cloud providers. As each provider offers different options for CPU, GPU, memory, and latency, it can be very difficult for users to decide which to choose. FogROS2-Sky includes an optimization algorithm, which either finds the best available hardware specification that fulfills the user's latency and cost constraints or reports that such a specification does not exist. We use FogROS2-Sky to perform time-cost analysis on three robotics applications: visual SLAM, grasp planning, and motion planning. We are able to sample different hardware setups at nearly half the cost while still create cost and latency functions suitable for the optimizer. We also evaluate the optimizer's efficacy for these applications with the Pareto frontier and show that the optimizer selects efficient hardware configurations to balance cost and latency. Videos and code are available on the website https://sites.google.com/view/fogros2-sky