Many robot tasks impose constraints on the workspace. For example, a robot may need to move a container without spilling its contents or open a door following the doorknob’s arc. Such constraints may induce narrow volumes in the configuration space, traditionally a challenge for sampling-based methods, and further cause infeasibility. We extend sample-driven connectivity learning (SDCL), a robust approach for planning with narrow passages, to develop a sampling ensemble for workspace constraints. In particular, the ensemble combines SDCL, projection via dual quaternion optimization, and random sampling. These complementary sampling approaches support efficient and robust planning under workspace constraints. Further, this framework offers the ability to determine infeasibility under workspace constraints, which is unaddressed by previous constrained planning methods.

Sampling-based motion planning works well in many cases but is less effective if the configuration space has narrow passages. In this paper, we propose a learning-based strategy to sample in these narrow passages, which improves overall planning time. Our algorithm first learns from the configuration space planning graphs and then uses the learned information to effectively generate narrow passage samples. We perform experiments in various 6D and 7D scenes. The algorithm offers one order of magnitude speed-up compared to baseline planners in some of these scenes.

In natural language learning, pre-trained language models (PLM) can acquire rich knowledge and concepts from rich corpora, making it possible to use PLM-based models for knowledge graph completion (KGC) tasks. However, in previous research, when applying pre-trained models to knowledge graph completion tasks, two main challenges persist: (1) Existing knowledge graph completion models are typically evaluated based on the closed-world assumption(CWA), thus lacking evaluation methods suitable for the open-world assumption(OWA), which constitutes a significant challenge in the current field of knowledge graph completion. (2) Extracting useful information, reducing noise, and providing clear interpretability for extracting effective information from the extensive prior knowledge embedded in pre-trained language models is also a crucial issue. Although the loss function can reduce noise to a certain extent, from the perspective of information theory, only relying on the loss function has a limited effect on noise reduction, and the model needs more professional tools to reduce noise and reduce the impact of irrelevant information on model performance. To address the aforementioned challenges, we propose a dual confrontation learning model based on the variational information bottleneck method. This model restricts information flow and feature selection from the perspective of information theory to reduce noise and enhance model performance while providing clear interpretability for this process. Based on extensive experiments and comprehensive evaluations conducted under both closed-world and open-world assumptions, this model successfully extracts valuable knowledge from pre-trained language models to accomplish KGC tasks. Simultaneously, it minimizes noise, removes non-robust features, enhances model reliability, and optimizes model performance. More importantly, we offer a strong interpretability for the process in which our model constrains information flow to reduce noise.

In this paper, we explore how robots can properly explain failures during navigation tasks with privacy concerns. We present an integrated robotics approach to generate visual failure explanations, by combining a language-capable cognitive architecture (for recognizing intent behind commands), an object- and location-based context recognition system (for identifying the locations of people and classifying the context in which those people are situated) and an infeasibility proof-based motion planner (for explaining planning failures on the basis of contextually mediated privacy concerns). The behavior of this integrated system is validated using a series of experiments in a simulated medical environment.

Acceptance of social robots in human-robot collaborative environments depends on the robots’ sensitivity to human moral and social norms. Robot behavior that violates norms may decrease trust and lead human interactants to blame the robot and view it negatively. Hence, for long-term acceptance, social robots need to detect possible norm violations in their action plans and refuse to perform such plans. This paper integrates the Distributed, Integrated, Affect, Reflection, Cognition (DIARC) robot architecture (implemented in the Agent Development Environment (ADE)) with a novel place recognition module and a norm-aware task planner to achieve context-sensitive moral reasoning. This will allow the robot to reject inappropriate commands and comply with context-sensitive norms. In a validation scenario, our results show that the robot would not comply with a human command to violate a privacy norm in a private context.

We present a general approach for constructing proofs of motion planning infeasibility. Effective high-dimensional motion planners, such as sampling-based methods, are limited to probabilistic completeness, so when no plan exists, these planners either do not terminate or can only run until a timeout. We address this completeness challenge by augmenting a sampling-based planner with a method to create an infeasibility proof in conjunction with building the search tree. An infeasibility proof is a closed polytope that separates the start and goal into disconnected components of the free configuration space. We identify possible facets of the polytope via a nonlinear optimization procedure using sampled points in the non-free configuration space. We identify the set of facets forming the separating polytope via a linear constraint satisfaction problem. This proof construction is valid for general (i.e., non-Cartesian) configuration spaces. We demonstrate this approach on the low-dimensional Jaco manipulator and discuss engineering approaches to scale to higher dimensional spaces.