iRoCo: Intuitive Robot Control From Anywhere Using a Smartwatch

This paper introduces iRoCo (intuitive Robot Control) - a framework for ubiquitous human-robot collaboration using a single smartwatch and smartphone. By integrating probabilistic differentiable filters, iRoCo optimizes a combination of precise robot control and unrestricted user movement from ubiquitous devices. We demonstrate and evaluate the effectiveness of iRoCo in practical teleoperation and drone piloting applications. Comparative analysis shows no significant difference between task performance with iRoCo and gold-standard control systems in teleoperation tasks. Additionally, iRoCo users complete drone piloting tasks 32\% faster than with a traditional remote control and report less frustration in a subjective load index questionnaire. Our findings strongly suggest that iRoCo is a promising new approach for intuitive robot control through smartwatches and smartphones from anywhere, at any time. The code is available at www.github.com/wearable-motion-capture

"Task Success" is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors

Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences. Their full power is better harnessed when the models are coupled with external verifiers and the final solutions are derived iteratively or progressively according to the verification feedback. In the context of embodied AI, verification often solely involves assessing whether goal conditions specified in the instructions have been met. Nonetheless, for these agents to be seamlessly integrated into daily life, it is crucial to account for a broader range of constraints and preferences beyond bare task success (e.g., a robot should grasp bread with care to avoid significant deformations). However, given the unbounded scope of robot tasks, it is infeasible to construct scripted verifiers akin to those used for explicit-knowledge tasks like the game of Go and theorem proving. This begs the question: when no sound verifier is available, can we use large vision and language models (VLMs), which are approximately omniscient, as scalable Behavior Critics to catch undesirable robot behaviors in videos? To answer this, we first construct a benchmark that contains diverse cases of goal-reaching yet undesirable robot policies. Then, we comprehensively evaluate VLM critics to gain a deeper understanding of their strengths and failure modes. Based on the evaluation, we provide guidelines on how to effectively utilize VLM critiques and showcase a practical way to integrate the feedback into an iterative process of policy refinement. The dataset and codebase are released at: https://guansuns.github.io/pages/vlm-critic.

Multimodal Learning of Soft Robot Dynamics using Differentiable Filters

Differentiable Filters, as recursive Bayesian estimators, possess the ability to learn complex dynamics by deriving state transition and measurement models exclusively from data. This data-driven approach eliminates the reliance on explicit analytical models while maintaining the essential algorithmic components of the filtering process. However, the gain mechanism remains non-differentiable, limiting its adaptability to specific task requirements and contextual variations. To address this limitation, this paper introduces an innovative approach called {\alpha}-MDF (Attention-based Multimodal Differentiable Filter). {\alpha}-MDF leverages modern attention mechanisms to learn multimodal latent representations for accurate state estimation in soft robots. By incorporating attention mechanisms, {\alpha}-MDF offers the flexibility to tailor the gain mechanism to the unique nature of the task and context. The effectiveness of {\alpha}-MDF is validated through real-world state estimation tasks on soft robots. Our experimental results demonstrate significant reductions in state estimation errors, consistently surpassing differentiable filter baselines by up to 45% in the domain of soft robotics.

Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train generalist X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. More details can be found on the project website $\href{https://robotics-transformer-x.github.io}{\text{robotics-transformer-x.github.io}}$.

Probabilistic Differentiable Filters Enable Ubiquitous Robot Control with Smartwatches

Ubiquitous robot control and human-robot collaboration using smart devices poses a challenging problem primarily due to strict accuracy requirements and sparse information. This paper presents a novel approach that incorporates a probabilistic differentiable filter, specifically the Differentiable Ensemble Kalman Filter (DEnKF), to facilitate robot control solely using Inertial Measurement Units (IMUs) observations from a smartwatch and a smartphone. The implemented system achieves accurate estimation of human pose state with a reduction of 30.2% compared to the baseline using the Mean Per Joint Vertex Error (MPJVE). Our results foster smartwatches and smartphones as a cost-effective alternative human-pose state estimation. Furthermore, experiment results from human-robot handover tasks underscore that smart devices allow for low-cost, versatile and ubiquitous robot control.

Projecting Robot Intentions Through Visual Cues: Static vs. Dynamic Signaling

Augmented and mixed-reality techniques harbor a great potential for improving human-robot collaboration. Visual signals and cues may be projected to a human partner in order to explicitly communicate robot intentions and goals. However, it is unclear what type of signals support such a process and whether signals can be combined without adding additional cognitive stress to the partner. This paper focuses on identifying the effective types of visual signals and quantify their impact through empirical evaluations. In particular, the study compares static and dynamic visual signals within a collaborative object sorting task and assesses their ability to shape human behavior. Furthermore, an information-theoretic analysis is performed to numerically quantify the degree of information transfer between visual signals and human behavior. The results of a human subject experiment show that there are significant advantages to combining multiple visual signals within a single task, i.e., increased task efficiency and reduced cognitive load.

Enhancing State Estimation in Robots: A Data-Driven Approach with Differentiable Ensemble Kalman Filters

This paper introduces a novel state estimation framework for robots using differentiable ensemble Kalman filters (DEnKF). DEnKF is a reformulation of the traditional ensemble Kalman filter that employs stochastic neural networks to model the process noise implicitly. Our work is an extension of previous research on differentiable filters, which has provided a strong foundation for our modular and end-to-end differentiable framework. This framework enables each component of the system to function independently, leading to improved flexibility and versatility in implementation. Through a series of experiments, we demonstrate the flexibility of this model across a diverse set of real-world tracking tasks, including visual odometry and robot manipulation. Moreover, we show that our model effectively handles noisy observations, is robust in the absence of observations, and outperforms state-of-the-art differentiable filters in terms of error metrics. Specifically, we observe a significant improvement of at least 59% in translational error when using DEnKF with noisy observations. Our results underscore the potential of DEnKF in advancing state estimation for robotics. Code for DEnKF is available at https://github.com/ir-lab/DEnKF

Learning Soft Robot Dynamics using Differentiable Kalman Filters and Spatio-Temporal Embeddings

This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal behavior of the robot. A novel spatio-temporal embedding process is discussed to handle observations with varying sensor placements and sampling frequencies. The efficacy of this approach is demonstrated on a tensegrity robot arm by learning end-effector dynamics from demonstrations with complex bending motions. The model is proven to be robust against missing modalities, diverse sensor placement, and varying sampling rates. Additionally, the proposed framework is shown to identify physical interactions with humans during motion. The utilization of a differentiable filter presents a novel solution to the difficulties of modeling soft robot dynamics. Our approach shows substantial improvement in accuracy compared to state-of-the-art filtering methods, with at least a 24% reduction in mean absolute error (MAE) observed. Furthermore, the predicted end-effector positions show an average MAE of 25.77mm from the ground truth, highlighting the advantage of our approach. The code is available at https://github.com/ir-lab/soft_robot_DEnKF.

Anytime, Anywhere: Human Arm Pose from Smartwatch Data for Ubiquitous Robot Control and Teleoperation

This work devises an optimized machine learning approach for human arm pose estimation from a single smartwatch. Our approach results in a distribution of possible wrist and elbow positions, which allows for a measure of uncertainty and the detection of multiple possible arm posture solutions, i.e., multimodal pose distributions. Combining estimated arm postures with speech recognition, we turn the smartwatch into a ubiquitous, low-cost and versatile robot control interface. We demonstrate in two use-cases that this intuitive control interface enables users to swiftly intervene in robot behavior, to temporarily adjust their goal, or to train completely new control policies by imitation. Extensive experiments show that the approach results in a 40% reduction in prediction error over the current state-of-the-art and achieves a mean error of 2.56cm for wrist and elbow positions.