Reviewerly/exHarmony huggingface dataset:
LEARNEST: LEARNing Enhanced Model-based State ESTimation for Robots using Knowledge-based Neural Ordinary Differential Equations

State estimation is an important aspect in many robotics applications. In this work, we consider the task of obtaining accurate state estimates for robotic systems by enhancing the dynamics model used in state estimation algorithms. Existing frameworks such as moving horizon estimation (MHE) and the unscented Kalman filter (UKF) provide the flexibility to incorporate nonlinear dynamics and measurement models. However, this implies that the dynamics model within these algorithms has to be sufficiently accurate in order to warrant the accuracy of the state estimates. To enhance the dynamics models and improve the estimation accuracy, we utilize a deep learning framework known as knowledge-based neural ordinary differential equations (KNODEs). The KNODE framework embeds prior knowledge into the training procedure and synthesizes an accurate hybrid model by fusing a prior first-principles model with a neural ordinary differential equation (NODE) model. In our proposed LEARNEST framework, we integrate the data-driven model into two novel model-based state estimation algorithms, which are denoted as KNODE-MHE and KNODE-UKF. These two algorithms are compared against their conventional counterparts across a number of robotic applications; state estimation for a cartpole system using partial measurements, localization for a ground robot, as well as state estimation for a quadrotor. Through simulations and tests using real-world experimental data, we demonstrate the versatility and efficacy of the proposed learning-enhanced state estimation framework.
Learning Augmented, Multi-Robot Long-Horizon Navigation in Partially Mapped Environments

We present a novel approach for efficient and reliable goal-directed long-horizon navigation for a multi-robot team in a structured, unknown environment by predicting statistics of unknown space. Building on recent work in learning-augmented model based planning under uncertainty, we introduce a high-level state and action abstraction that lets us approximate the challenging Dec-POMDP into a tractable stochastic MDP. Our Multi-Robot Learning over Subgoals Planner (MR-LSP) guides agents towards coordinated exploration of regions more likely to reach the unseen goal. We demonstrate improvement in cost against other multi-robot strategies; in simulated office-like environments, we show that our approach saves 13.29% (2 robot) and 4.6% (3 robot) average cost versus standard non-learned optimistic planning and a learning-informed baseline.
Robust Human Pose Estimation under Gaussian Noise

Robustness against specific kinds of noise is of high importance for safety-critical components in industrial robot applications, as legal and normative regulations demand the identification and handling of all unacceptable risks. This includes risks from environmental conditions, like noisy data. One such component is human pose estimation, which is needed and crucial for human-robot collaboration tasks and applications. However, little research on human pose estimation under specific noise types has been performed. In our work, we focus on extensively evaluating human pose estimation under specific noise and propose potential countermeasures. We leverage Gaussian noise as specific noise type and the hourglass model as human pose estimator. We show that human pose estimation is already vulnerable to small amounts of Gaussian noise. As countermeasures we propose either denoising images upfront or training the hourglass model to be robust against Gaussian noise. All methods achieve a significantly higher robustness against Gaussian noise, typically at the cost of slightly worse performance on clean data. Three of our methods also achieved slight improvements on clean data.
Lidar Augment: Searching for Scalable 3D LiDAR Data Augmentations

Data augmentations are important for training high-performance 3D object detectors that use point clouds. Despite recent efforts on designing new data augmentations, perhaps surprisingly, most current state-of-the-art 3D detectors only rely on a few simple data augmentations. In particular, different from 2D image data augmentations, 3D data augmentations need to account for different representations of input data and require being customized for different models, which introduces significant overhead. In this paper, we propose LidarAugment, a practical and effective data augmentation strategy for 3D object detection. Unlike previous methods, which require tuning all augmentation policies in an exponentially large search space, we propose to factorize and align the search space of each data augmentation, which cuts down the 20+ hyperparameters to 2, and significantly reduces the search complexity. We show LidarAugment can be easily adapted to different model architectures with different input representations by a simple 2D grid search, and consistently improve a range of detectors including both convolution-based UPillars/StarNet/RSN and transformer-based SWFormer. Furthermore, Lidar Augment mitigates overfitting and enables 3D detectors to scale up to larger capacities. When combined with the latest 3D detectors, Lidar Augment achieves a new state-of-the-art 74.8 mAPH L2 on the Waymo Open Dataset.
A Linear and Exact Algorithm for Whole-Body Collision Evaluation via Scale Optimization

Collision evaluation is of essential importance in various applications. However, existing methods are either cumbersome to calculate or not exact. Therefore, considering the cost of implementation, most whole-body planning works, which require evaluating collision between robots and environments, struggle to tradeoff between accuracy and computationally efficiency. In this paper, we propose a zero-gap whole-body collision evaluation that can be formulated as a low-dimensional linear programming. This evaluation can be solved analytically in linear complexity. Moreover, the method provides gradient efficiently, making it accessible to optimization-based applications. Additionally, this method provides support for obstacles represented by either points or hyperplanes. Experiments on the widely used aerial and car-like robots validate the versatility and practicality of our method.
Operative Action Captioning for Estimating System Actions

Human-assistive systems, such as robots, need to correctly understand the surrounding situation based on obser-vations and output the required support actions for humans. Language is one of the important channels to communicate with humans, and robots are required to have the ability to express their understanding and action-planning results. In this study, we propose a new task of operative action captioning that estimates and verbalizes the actions to be taken by the system in a human-assisting domain. We constructed a system that outputs a verbal description of a possible operative action that changes the current state to the given target state. We collected a dataset consisting of two images as observations, which express the current state and the state changed by actions and a caption that describes the actions that change the current state to the target state, by crowdsourcing in daily life situations. Then we constructed a system that estimates an operative action by a caption. Since the operative action's caption is expected to contain some state-changing actions, we use scene graph prediction as an auxiliary task because the events written in the scene graphs correspond to the state changes. Experimental results showed that our system successfully described the operative actions that should be conducted between the current and target states. The auxiliary tasks that predict the scene graphs improved the quality of the estimation results.
Lightweight Monocular Depth Estimation via Token-Sharing Transformer

Depth estimation is an important task in various robotics systems and applications. In mobile robotics systems, monocular depth estimation is desirable since a single RGB camera can be deployable at a low cost and compact size. Due to its significant and growing needs, many lightweight monocular depth estimation networks have been proposed for mobile robotics systems. While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently. However, massive parameters and large computational costs in the Transformer disturb the deployment to embedded devices. In this paper, we present a Token-Sharing Transformer (TST), an architecture using the Transformer for monocular depth estimation, optimized especially in embedded devices. The proposed TST utilizes global token sharing, which enables the model to obtain an accurate depth prediction with high throughput in embedded devices. Experimental results show that TST outperforms the existing lightweight monocular depth estimation methods. On the NYU Depth v2 dataset, TST can deliver depth maps up to 63.4 FPS in NVIDIA Jetson nano and 142.6 FPS in NVIDIA Jetson TX2, with lower errors than the existing methods. Furthermore, TST achieves real-time depth estimation of high-resolution images on Jetson TX2 with competitive results.
Handling Sparse Rewards in Reinforcement Learning Using Model Predictive Control

Reinforcement learning (RL) has recently proven great success in various domains. Yet, the design of the reward function requires detailed domain expertise and tedious fine-tuning to ensure that agents are able to learn the desired behaviour. Using a sparse reward conveniently mitigates these challenges. However, the sparse reward represents a challenge on its own, often resulting in unsuccessful training of the agent. In this paper, we therefore address the sparse reward problem in RL. Our goal is to find an effective alternative to reward shaping, without using costly human demonstrations, that would also be applicable to a wide range of domains. Hence, we propose to use model predictive control (MPC) as an experience source for training RL agents in sparse reward environments. Without the need for reward shaping, we successfully apply our approach in the field of mobile robot navigation both in simulation and real-world experiments with a Kuboki Turtlebot 2. We furthermore demonstrate great improvement over pure RL algorithms in terms of success rate as well as number of collisions and timeouts. Our experiments show that MPC as an experience source improves the agent's learning process for a given task in the case of sparse rewards.
Aligning Human Preferences with Baseline Objectives in Reinforcement Learning

Practical implementations of deep reinforcement learning (deep RL) have been challenging due to an amplitude of factors, such as designing reward functions that cover every possible interaction.To address the heavy burden of robot reward engineering, we aim to leverage subjective human preferences gathered in the context of human-robot interaction, while taking advantage of a baseline reward function when available.By considering baseline objectives to be designed beforehand, we are able to narrow down the policy space, solely requesting human attention when their input matters the most.To allow for control over the optimization of different objectives, our approach contemplates a multi-objective setting.We achieve human-compliant policies by sequentially training an optimal policy from a baseline specification and collecting queries on pairs of trajectories.These policies are obtained by training a reward estimator to generate Pareto optimal policies that include human preferred behaviours.Our approach ensures sample efficiency and we conducted a user study to collect real human preferences, which we utilized to obtain a policy on a social navigation environment.
EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics

We study the problem of learning graph dynamics of deformable objects that generalizes to unknown physical properties. Our key insight is to leverage a latent representation of elastic physical properties of cloth-like deformable objects that can be extracted, for example, from a pulling interaction. In this paper we propose EDO-Net (Elastic Deformable Object - Net), a model of graph dynamics trained on a large variety of samples with different elastic properties that does not rely on ground-truth labels of the properties. EDO-Net jointly learns an adaptation module, and a forward-dynamics module. The former is responsible for extracting a latent representation of the physical properties of the object, while the latter leverages the latent representation to predict future states of cloth-like objects represented as graphs. We evaluate EDO-Net both in simulation and real world, assessing its capabilities of: 1) generalizing to unknown physical properties, 2) transferring the learned representation to new downstream tasks.
LEMURS: Learning Distributed Multi-Robot Interactions

This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies from cooperative task demonstrations. We propose a port-Hamiltonian description of the multi-robot system to exploit universal physical constraints in interconnected systems and achieve closed-loop stability. We represent a multi-robot control policy using an architecture that combines self-attention mechanisms and neural ordinary differential equations. The former handles time-varying communication in the robot team, while the latter respects the continuous-time robot dynamics. Our representation is distributed by construction, enabling the learned control policies to be deployed in robot teams of different sizes. We demonstrate that LEMURS can learn interactions and cooperative behaviors from demonstrations of multi-agent navigation and flocking tasks.
Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation

Safety is essential for deploying Deep Reinforcement Learning (DRL) algorithms in real-world scenarios. Recently, verification approaches have been proposed to allow quantifying the number of violations of a DRL policy over input-output relationships, called properties. However, such properties are hard-coded and require task-level knowledge, making their application intractable in challenging safety-critical tasks. To this end, we introduce the Collection and Refinement of Online Properties (CROP) framework to design properties at training time. CROP employs a cost signal to identify unsafe interactions and use them to shape safety properties. Hence, we propose a refinement strategy to combine properties that model similar unsafe interactions. Our evaluation compares the benefits of computing the number of violations using standard hard-coded properties and the ones generated with CROP. We evaluate our approach in several robotic mapless navigation tasks and demonstrate that the violation metric computed with CROP allows higher returns and lower violations over previous Safe DRL approaches.
Test-Time Synthetic-to-Real Adaptive Depth Estimation

Is it possible for a synthetic to realistic domain adapted neural network in single image depth estimation to truly generalize on real world data? The resultant, adapted model will only generalize on the realistic domain dataset, which only reflects a small portion of the true, real world. As a result, the network still has to cope with the potential danger of domain shift between the realistic domain dataset and the real world data. Instead, a viable solution is to design the model to be capable of continuously adapting to the distribution of data it receives at test-time. In this paper, we propose a depth estimation method that is capable of adapting to the domain shift at test-time. Our method adapts to the unseen test-time domain, by updating the network using our proposed objective functions. Following former work, we reduce the entropy of the current prediction for refinement and adaptation. We propose a Logit Order Enforcement loss that can prevent the network from deviating into wrong solutions, which can result from the mere reduction of the aforementioned entropy. Qualitative and quantitative results show the effectiveness of our method. Our method reduces the dependency on training data by 5.8× on average, while achieving comparable performance to state-of-the-art unsupervised domain adaptation (UDA) and domain generalization methods (DG) on the KITTI dataset.
Safe and Distributed Multi-Agent Motion Planning under Minimum Speed Constraints

The motion planning problem for multiple unstop-pable agents is of interest in many robotics applications, for example, autonomous traffic management for multiple fixed-wing aircraft. Unfortunately, many of the existing algorithms cannot provide safety for such agents, because they require the agents to be able to brake to a complete stop for safety and feasibility insurance. In this paper, we present a distributed multi-agent motion planner that guarantees collision avoidance and persistent feasibility, which can be applied to a team of homogeneous mobile vehicles that cannot stop. The planner is built on top of the idea that a collision-free trajectory in form of a loop can safely accommodate multiple unstoppable agents, while avoiding collisions among them and static obstacles. At every time step, in a distributed manner, the agents generate trajectory-manipulating actions that preserve the loop structure. Then, a deconfliction process selects a conflict-free subset of the generated actions, which are applied at the next time step. Through simulation using an unstoppable Dubins car model, we show that the proposed motion planner is able to provide persistent safety guarantees for such agents in obstacle-cluttered space in real-time.
Adaptive approximation of dynamics gradients via interpolation to speed up trajectory optimisation

Trajectory optimisation methods for robotic motion planning often require the use of first order derivatives of the dynamics of the system with respect to the states and controls of the system. Particularly when multi-contact dynamics are present, these derivatives are often numerically approximated by a method such as finite-differencing. Finite-differencing whilst using an expensive physics simulator is usually the bottleneck in these trajectory optimisation algorithms. Since these dynamics derivatives do not change substantially over certain time inter-vals, we propose that trajectory optimisers can compute the dy-namics derivatives less often and then interpolate approximations to the derivatives in between calculated derivatives, gaining a sig-nificant speed up for overall optimisation time with no observable degradation in the generated behaviour. We investigate different methods of interpolating approximations as well as propose an adaptive method to detect when to compute the derivatives with finite-differencing. We find a speed-up of planning times on average by 60% in a contact-based manipulation task.
Self-Entanglement-Free Tethered Path Planning for Non-Particle Differential-Driven Robot

A novel mechanism to derive self-entanglement-free path for tethered differential-driven robots is proposed in this work. The problem is tailored to the applications of tethered robots without an omni-directional tether re-tractor which is often encountered when an omni-directional tether retracting mechanism is incapable of being jointly equipped with other geometrically complex devices (e.g. a manipulator), for instance the disaster recovery, spatial exploration, etc. Without a special consideration on the spatial relation between the pose of the mobile base and the tether, self-entanglement appears when the robot moves, resulting in unsafe motion of the robot and potential damage to the tether. In this paper, the self-entanglement-free constraint is modelled as the admissible orientation of the tether anchoring on the robot with respect to the robot's heading orientation. A searching-based path planning algorithm is then proposed to generate a near optimal path solution with guaranteed null of tether self-entanglement. The effectiveness of the proposed algorithm is compared with the motions without considering self-entanglement-free constraint, illustrated in challenging planning cases, and validated in realworld scenes. An open-source implementation has also been provided for the benefit of the robotics community.
Data-Efficient Characterization of the Global Dynamics of Robot Controllers with Confidence Guarantees

This paper proposes an integration of surrogate modeling and topology to significantly reduce the amount of data required to describe the underlying global dynamics of robot controllers, including closed-box ones. A Gaussian Process (GP), trained with randomized short trajectories over the state-space, acts as a surrogate model for the underlying dynamical system. Then, a combinatorial representation is built and used to describe the dynamics in the form of a directed acyclic graph, known as Morse graph. The Morse graph is able to describe the system's attractors and their corresponding regions of attraction (RoA). Furthermore, a pointwise confidence level of the global dynamics estimation over the entire state space is provided. In contrast to alternatives, the framework does not require estimation of Lyapunov functions, alleviating the need for high prediction accuracy of the GP. The framework is suit-able for data-driven controllers that do not expose an analytical model as long as Lipschitz-continuity is satisfied. The method is compared against established analytical and recent machine learning alternatives for estimating Roas, outperforming them in data efficiency without sacrificing accuracy. Link to code: https://go.rutgers.edu/49hy35en
Continuous-Time Gaussian Process Motion-Compensation for Event-Vision Pattern Tracking with Distance Fields

This work addresses the issue of motion compensation and pattern tracking in event camera data. An event camera generates asynchronous streams of events triggered independently by each of the pixels upon changes in the observed intensity. Providing great advantages in low-light and rapid-motion scenarios, such unconventional data present significant research challenges as traditional vision algorithms are not directly applicable to this sensing modality. The proposed method decomposes the tracking problem into a local SE(2) motion-compensation step followed by a homography registration of small motion-compensated event batches. The first component relies on Gaussian Process (GP) theory to model the continuous occupancy field of the events in the image plane and embed the camera trajectory in the covariance kernel function. In doing so, estimating the trajectory is done similarly to GP hyperparameter learning by maximising the log marginal likelihood of the data. The continuous occupancy fields are turned into distance fields and used as templates for homography-based registration. By benchmarking the proposed method against other state-of-the-art techniques, we show that our open-source implementation performs high-accuracy motion compensation and produces high-quality tracks in real-world scenarios.