Chemistry Foundation Models (CFMs) that leverage Graph Neural Networks (GNNs) operating on 3D molecular graph structures are becoming indispensable tools for computational chemists and materials scientists. These models facilitate the understanding of matter and the discovery of new molecules and materials. In contrast to GNNs operating on large homogeneous graphs, GNNs used by CFMs process a large number of geometric graphs of varying sizes, requiring different optimization strategies than those developed for large homogeneous GNNs. This paper presents optimizations for two critical phases of CFM training: data distribution and model training, targeting MACE - a state-of-the-art CFM. We address the challenge of load balancing in data distribution by formulating it as a multi-objective bin packing problem. We propose an iterative algorithm that provides a highly effective, fast, and practical solution, ensuring efficient data distribution. For the training phase, we identify symmetric tensor contraction as the key computational kernel in MACE and optimize this kernel to improve the overall performance. Our combined approach of balanced data distribution and kernel optimization significantly enhances the training process of MACE. Experimental results demonstrate a substantial speedup, reducing per-epoch execution time for training from 12 to 2 minutes on 740 GPUs with a 2.6M sample dataset.

Safe quadrupedal navigation through unknown environments is a challenging problem. This paper proposes a hierarchical vision-based planning framework (GPF-BG) integrating our previous Global Path Follower (GPF) navigation system and a gap-based local planner using Bézier curves, so called$B$ézier Gap (BG). This BG-based trajectory synthesis can generate smooth trajectories and guarantee safety for point-mass robots. With a gap analysis extension based on non-point, rectangular geometry, safety is guaranteed for an idealized quadrupedal motion model and significantly improved for an actual quadrupedal robot model. Stabilized perception space improves performance under oscillatory internal body motions that impact sensing. Simulation-based and real experiments under different benchmarking configurations test safe navigation performance. GPF-BG has the best safety outcomes across all experiments.

Real-time navigation in non-trivial environments by micro aerial vehicles (MAVs) predominantly relies on modelling the MAV with idealized geometry, such as a sphere. Simplified, conservative representations increase the likelihood of a planner failing to identify valid paths. That likelihood increases the more a robot's geometry differs from the idealized version. Few current approaches consider these situations; we are unaware of any that do so using perception space representations. This work introduces the egocan, a perception space obstacle representation using line-of-sight free space estimates, and 3D Gap, a perception space approach to gap finding for identifying goal-directed, collision-free directions of travel through 3D space. Both are integrated, with real-time considerations in mind, to define a local planner module of a hierarchical navigation system. The result is Aerial Local Planning in Perception Space (AeriaLPiPS). AeriaLPiPS is shown to be capable of safely navigating a MAV with non-idealized geometry through various environments, including those impassable by traditional real-time approaches. The open source implementation of this work is available at github.com/ivaROS/AeriaLPiPS.

The advent of deep learning has inspired research into end-to-end learning for a variety of problem domains in robotics. For navigation, the resulting methods may not have the generalization properties desired let alone match the performance of traditional methods. Instead of learning a navigation policy, we explore learning an adaptive policy in the parameter space of an existing navigation module. Having adaptive parameters provides the navigation module with a family of policies that can be dynamically reconfigured based on the local scene structure and addresses the common assertion in machine learning that engineered solutions are inflexible. Of the methods tested, reinforcement learning (RL) is shown to provide a significant performance boost to a modern navigation method through reduced sensitivity of its success rate to environmental clutter. The outcomes indicate that RL as a meta-policy learner, or dynamic parameter tuner, effectively robustifies algorithms sensitive to external, measurable nuisance factors.

The TEB hierarchical planner for real-time navigation through unknown environments is highly effective at balancing collision avoidance with goal directed motion. Designed over several years and publications, it implements a multi-trajectory optimization based synthesis method for identifying topologically distinct trajectory candidates through navigable space. Unfortunately, the underlying factor graph approach to the optimization problem induces a mismatch between grid-based representations and the optimization graph, which leads to several time and optimization inefficiencies. This paper explores the impact of using egocentric, perception space representations for the local planning map. Doing so alleviates many of the identified issues related to TEB and leads to a new method called egoTEB. Timing experiments and Monte Carlo evaluations in benchmark worlds quantify the benefits of egoTEB for navigation through uncertain environments.

Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e.g. indoor, underground. Conventionally, the performance of visual-inertial SLAM is evaluated with open-loop analysis, with a focus on the drift level of SLAM systems. In this paper, we raise the question on the importance of visual estimation latency in closed-loop navigation tasks, such as accurate trajectory tracking. To understand the impact of both drift and latency on visualinertial SLAM systems, a closed-loop benchmarking simulation is conducted, where a robot is commanded to follow a desired trajectory using the feedback from visual-inertial estimation. By extensively evaluating the trajectory tracking performance of representative state-of-the-art visual-inertial SLAM systems, we reveal the importance of latency reduction in visual estimation module of these systems. The findings suggest directions of future improvements for visual-inertial SLAM.

Path planning for mobile robots requires rapidly finding collision-free trajectories in an uncertain and changing environment. Full collision checking with detailed, online-revised representations of the robot and world imposes a delay that undermines reactive obstacle avoidance. As a result, reactive vision-based approaches make various assumptions to arrive at simplified representations, such as circular or spherical robot shapes reducible to point masses, or obstacles that always rise from the ground. We seek to avoid these problems by modeling the robot directly in perception space so that collisionfree trajectories can be sought in a consistent representation with minimal processing needs. Here perception space refers to the depth space image measurements available by modern consumer range sensors. We hallucinate a robot navigating through the world and synthesize depth images of its path for comparison against the directly sensed depth images of the local world. The approach performs collision checking in a 3D volume but only requires 2D image comparisons. Experiments show that an implementation is able to negotiate an obstacle course consisting of miscellaneous objects in real-time.

In order for robots to work alongside humans in a range of domains, they will need to operate with a variety of social dynamics that each context will require. This paper builds on previous work with a parameterized turn-taking model, CADENCE, in which different parameter settings resulted in different social dynamics. In contrast to the static parameter settings of previous work, we now investigate the problem of changing these turn-taking parameter sets dynamically within a single interaction session. This ability is necessary for successful peer-to-peer collaborations, in which balance of control between leading and following must be maintained. We present our dynamic switching approach and an experiment with 15 participants. Our results confirm that it is possible to achieve the same changes in social dynamics within a single interaction session that were previously seen only between independent sessions of different parameter settings. Moreover, we show that such a change in social dynamics is contingent upon changing parameters at socially appropriate turn boundaries.