Sim2real, that is, the transfer of learned control policies from simulation to the real world, is an area of growing interest in robotics because of its potential to efficiently handle complex tasks. The sim2real approach faces challenges because of mismatches between simulation and reality. These discrepancies arise from inaccuracies in modeling physical phenomena and asynchronous control, among other factors. To this end, we introduce Engine Agnostic Graph Environments for Robotics (EAGERx), a framework with a unified software pipeline for both real and simulated robot learning. It can support various simulators and aids in integrating state, action, and time scale abstractions to facilitate learning. EAGERx’s integrated delay simulation, domain randomization features, and proposed synchronization algorithm contribute to narrowing the sim2real gap. We demonstrate (in the context of robot learning and beyond) the efficacy of EAGERx in accommodating diverse robotic systems and maintaining consistent simulation behavior. EAGERx is open source, and its code is available at https://eagerx.readthedocs.io

Human teaching effort is a significant bottleneck for the broader applicability of interactive imitation learning. To reduce the number of required queries, existing methods employ active learning to query the human teacher only in uncertain, risky, or novel situations. However, during these queries, the novice’s planned actions are not utilized despite containing valuable information, such as the novice’s capabilities, as well as corresponding uncertainty levels. To this end, we allow the novice to say: “I plan to do this, but I am uncertain.” We introduce the Active Skill-level Data Aggregation (ASkDAgger) framework, which leverages teacher feedback on the novice plan in three key ways: (1) S-Aware Gating (SAG): Adjusts the gating threshold to track sensitivity, specificity, or a minimum success rate; (2) Foresight Interactive Experience Replay (FIER), which recasts valid and relabeled novice action plans into demonstrations; and (3) Prioritized Interactive Experience Replay (PIER), which prioritizes replay based on uncertainty, novice success, and demonstration age. Together, these components balance query frequency with failure incidence, reduce the number of required demonstration annotations, improve generalization, and speed up adaptation to changing domains. We validate the effectiveness of ASkDAgger through language-conditioned manipulation tasks in both simulation and real-world environments. Code, data, and videos are available at https://askdagger.github.io.

Several recent works show impressive results in mapping language-based human commands and image scene observations to direct robot executable policies (e.g., pick and place poses). However, these approaches do not consider the uncertainty of the trained policy and simply always execute actions that are suggested by the current policy as the most probable ones. This makes them vulnerable to domain shift and inefficient in the number of required demonstrations. We extend previous works and present the PARTNR algorithm that can detect ambiguities in the trained policy by analyzing multiple modes in the probability distributio of pick and place poses using topological analysis. In this way uncertainty in action can be estimated with single inference (and training single model) instead of using ensemble of models. Additionally, PARTNR employs an adaptive, sensitivity-based, gating function that decides if additional user demonstrations are required. User demonstrations are aggregated to the dataset and used for subsequent training. In this way, the policy can adapt promptly to domain shift and it can minimize the number of required demonstrations for a well-trained policy. The adaptive threshold enables to achieve the user-acceptable level of ambiguity to execute the policy autonomously and in turn, increase the trustworthiness of our system. We demonstrate the performance of PARTNR in a table-top pick and place task.

In robot manipulation, Reinforcement Learning (RL) often suffers from low sample efficiency and uncertain convergence, especially in large observation and action spaces. Foundation Models (FMs) offer an alternative, demonstrating promise in zero-shot and few-shot settings. However, they can be unreliable due to limited physical and spatial understanding. We introduce ExploRLLM, a method that combines the strengths of both paradigms. In our approach, FMs improve RL convergence by generating policy code and efficient representations, while a residual RL agent compensates for the FMs' limited physical understanding. We show that Explorllm outperforms both policies derived from FMs and RL baselines in table-top manipulation tasks. Additionally, real-world experiments show that the policies exhibit promising zero-shot sim-to-real transfer. Supplementary material is available at https://explorllm.github.io.

To gently grasp objects, robots need to balance generating enough friction yet avoiding too much force that could damage the object. In practice, the force regulation is challenging to implement since it requires knowledge of the friction coefficient, which can vary from object to object and even from grasp to grasp. Tactile sensing offers a window in the contact mechanics and provides information about friction. Notably touch can detect the precursor of the object slipping away from the grasp. To find this information, tactile sensors measure the deformation field of an artificial skin in both the normal and tangential direction. However, current approaches only react to slip and therefore react too late to perturbations. The object slips, inducing a failure of the grasp and damage. In this study, we introduce a method that uses machine-learning to anticipate slip by computing the so-called safety margin of the grasp. This safety margin represents the extra lateral force that maintains the contact away from the frictional limit. To find this value, we use a high-density camera-based tactile sensor to measure the 3D deformation of the surface via the movement of 82 colored markers. We trained a Convolutional Neural Network (CNN) to estimate the safety margin from the tactile images. Because it gives a distance to slip, the safety margin is a powerful metric for regulating grasp forces. As a testament of this effectiveness, we show that a simple proportional controller can robustly grasp a wide variety of objects. The results show that this control method outperforms slip detection methods, by reducing regrasp reaction times while decreasing grasping forces to 1-3 N.

The up-and-coming concept of Industry 5.0 fore-sees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is crucial. Learning from demonstration emerged as the enabling approach to address such a problem. However, more focus should be put on finding safe solutions which optimize the cost associated with the demonstrations collection process. This paper introduces a preliminary outline of a system, namely EValueAction (EVA), designed to assist the human in the process of collecting interactive demonstrations taking advantage of simulation to safely avoid failures. A policy is pre-trained with human-demonstrations and, where needed, new informative data are interactively gathered and aggregated to iteratively improve the initial policy. A trial case study further reinforces the relevance of the work by demonstrating the crucial role of informative demonstrations for generalization.