Robotics (cs.RO)

Abstract: A key challenge in Bayesian decentralized data fusion is the `rumor propagation' or `double counting' phenomenon, where previously sent data circulates back to its sender. It is often addressed by approximate methods like covariance intersection (CI) which takes a weighted average of the estimates to compute the bound. The problem is that this bound is not tight, i.e. the estimate is often over-conservative. In this paper, we show that by exploiting the probabilistic independence structure in multi-agent decentralized fusion problems a tighter bound can be found using (i) an expansion to the CI algorithm that uses multiple (non-monolithic) weighting factors instead of one (monolithic) factor in the original CI and (ii) a general optimization scheme that is able to compute optimal bounds and fully exploit an arbitrary dependency structure. We compare our methods and show that on a simple problem, they converge to the same solution. We then test our new non-monolithic CI algorithm on a large-scale target tracking simulation and show that it achieves a tighter bound and a more accurate estimate compared to the original monolithic CI.

Abstract: Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computational complexity, dependencies on well-structured environments, etc. To overcome these limitations, we propose a new distributed approach to perform relative localization using a Gaussian Processes map of the Radio Signal Strength Indicator (RSSI) values from a single wireless Access Point (AP) to which the robots are connected. Our approach, Gaussian Processes-based Relative Localization (GPRL), combines two pillars. First, the robots locate the AP w.r.t. their local reference frames using novel hierarchical inferencing that significantly reduces computational complexity. Secondly, the robots obtain relative positions of neighbor robots with an AP-oriented vector transformation. The approach readily applies to resource-constrained devices and relies only on the ubiquitously-available RSSI measurement. We extensively validate the performance of the two pillars of the proposed GRPL in Robotarium simulations. We also demonstrate the applicability of GPRL through a multi-robot rendezvous task with a team of three real-world robots. The results demonstrate that GPRL outperformed state-of-the-art approaches regarding accuracy, computation, and real-time performance.

Abstract: As the advent of artificial general intelligence (AGI) progresses at a breathtaking pace, the application of large language models (LLMs) as AI Agents in robotics remains in its nascent stage. A significant concern that hampers the seamless integration of these AI Agents into robotics is the unpredictability of the content they generate, a phenomena known as ``hallucination''. Drawing inspiration from biological neural systems, we propose a novel, layered architecture for autonomous robotics, bridging AI agent intelligence and robot instinct. In this context, we define Robot Instinct as the innate or learned set of responses and priorities in an autonomous robotic system that ensures survival-essential tasks, such as safety assurance and obstacle avoidance, are carried out in a timely and effective manner. This paradigm harmoniously combines the intelligence of LLMs with the instinct of robotic behaviors, contributing to a more safe and versatile autonomous robotic system. As a case study, we illustrate this paradigm within the context of a mobile robot, demonstrating its potential to significantly enhance autonomous robotics and enabling a future where robots can operate independently and safely across diverse environments.

Abstract: Applications involving humans and robots working together are spreading nowadays. Alongside, modeling and control techniques that allow physical Human-Robot Interaction (pHRI) are widely investigated. To better understand its potential application in pHRI, this work investigates the Cooperative Differential Game Theory modeling of pHRI in a cooperative reaching task, specifically for reference tracking. The proposed controller based on Collaborative Game Theory is deeply analyzed and compared in simulations with two other techniques, Linear Quadratic Regulator (LQR) and Non-Cooperative Game-Theoretic Controller. The set of simulations shows how different tuning of control parameters affects the system response and control efforts of both the players for the three controllers, suggesting the use of Cooperative GT in the case the robot should assist the human, while Non-Cooperative GT represents a better choice in the case the robot should lead the action. Finally, preliminary tests with a trained human are performed to extract useful information on the real applicability and limitations of the proposed method.

Abstract: This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, human intention is defined as the desired trajectory that the human wants to follow over a finite rolling prediction horizon so that the robot can assist in pursuing it. This work investigates a Recurrent Neural Network (RNN), specifically, Long-Short Term Memory (LSTM) cascaded with a Fully Connected layer. In particular, we propose an iterative training procedure to adapt the model. Such an iterative procedure is powerful in reducing the prediction error. Still, it has the drawback that it is time-consuming and does not generalize to different users or different co-manipulated objects. To overcome this issue, Transfer Learning (TL) adapts the pre-trained model to new trajectories, users, and co-manipulated objects by freezing the LSTM layer and fine-tuning the last FC layer, which makes the procedure faster. Experiments show that the iterative procedure adapts the model and reduces prediction error. Experiments also show that TL adapts to different users and to the co-manipulation of a large object. Finally, to check the utility of adopting the proposed method, we compare the proposed controller enhanced by the intention prediction with the other two standard controllers of pHRI.

Abstract: Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to fabricate replaceable components, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 0.040 and 0.030 compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future.

Abstract: Goal-Conditioned Reinforcement Learning (GCRL) can enable agents to spontaneously set diverse goals to learn a set of skills. Despite the excellent works proposed in various fields, reaching distant goals in temporally extended tasks remains a challenge for GCRL. Current works tackled this problem by leveraging planning algorithms to plan intermediate subgoals to augment GCRL. Their methods need two crucial requirements: (i) a state representation space to search valid subgoals, and (ii) a distance function to measure the reachability of subgoals. However, they struggle to scale to high-dimensional state space due to their non-compact representations. Moreover, they cannot collect high-quality training data through standard GC policies, which results in an inaccurate distance function. Both affect the efficiency and performance of planning and policy learning. In the paper, we propose a goal-conditioned RL algorithm combined with Disentanglement-based Reachability Planning (REPlan) to solve temporally extended tasks. In REPlan, a Disentangled Representation Module (DRM) is proposed to learn compact representations which disentangle robot poses and object positions from high-dimensional observations in a self-supervised manner. A simple REachability discrimination Module (REM) is also designed to determine the temporal distance of subgoals. Moreover, REM computes intrinsic bonuses to encourage the collection of novel states for training. We evaluate our REPlan in three vision-based simulation tasks and one real-world task. The experiments demonstrate that our REPlan significantly outperforms the prior state-of-the-art methods in solving temporally extended tasks.

Abstract: Trajectory and control secrecy is an important issue in robotics security. This paper proposes a novel algorithm for the control input inference of a mobile agent without knowing its control objective. Specifically, the algorithm first estimates the target state by applying external perturbations. Then we identify the objective function based on the inverse optimal control, providing the well-posedness proof and the identifiability analysis. Next, we obtain the optimal estimate of the control horizon using binary search. Finally, the agent's control optimization problem is reconstructed and solved to predict its input. Simulation illustrates the efficiency and the performance of the algorithm.

Abstract: As social robots see increasing deployment within the general public, improving the interaction with those robots is essential. Spoken language offers an intuitive interface for the human-robot interaction (HRI), with dialogue management (DM) being a key component in those interactive systems. Yet, to overcome current challenges and manage smooth, informative and engaging interaction a more structural approach to combining HRI and DM is needed. In this systematic review, we analyse the current use of DM in HRI and focus on the type of dialogue manager used, its capabilities, evaluation methods and the challenges specific to DM in HRI. We identify the challenges and current scientific frontier related to the DM approach, interaction domain, robot appearance, physical situatedness and multimodality.

Abstract: In CMF surgery, the planning of bony movement to achieve a desired facial outcome is a challenging task. Current bone driven approaches focus on normalizing the bone with the expectation that the facial appearance will be corrected accordingly. However, due to the complex non-linear relationship between bony structure and facial soft-tissue, such bone-driven methods are insufficient to correct facial deformities. Despite efforts to simulate facial changes resulting from bony movement, surgical planning still relies on iterative revisions and educated guesses. To address these issues, we propose a soft-tissue driven framework that can automatically create and verify surgical plans. Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans. By combining these two models, we can verify and determine the final bony movement required for planning. The proposed framework was evaluated using a clinical dataset, and our experimental results demonstrate that the soft-tissue driven approach greatly improves the accuracy and efficacy of surgical planning when compared to the conventional bone-driven approach.