Much worldly semantic knowledge can be encoded in large language models (LLMs). Such information could be of great use to robots that want to carry out high-level, temporally extended commands stated in natural language. However, the lack of real-world experience that language models have is a key limitation that makes it challenging to use them for decision-making inside a particular embodiment. This research assesses the feasibility of using LLM (GPT-3.5-turbo chatbot by OpenAI) for robotic path planning. The shortcomings of conventional approaches to managing complex environments and developing trustworthy plans for shifting environmental conditions serve as the driving force behind the research. Due to the sophisticated natural language processing abilities of LLM, the capacity to provide effective and adaptive path-planning algorithms in real-time, great accuracy, and few-shot learning capabilities, GPT-3.5-turbo is well suited for path planning in robotics. In numerous simulated scenarios, the research compares the performance of GPT-3.5-turbo with that of state-of-the-art path planners like Rapidly Exploring Random Tree (RRT) and A*. We observed that GPT-3.5-turbo is able to provide real-time path planning feedback to the robot and outperforms its counterparts. This paper establishes the foundation for LLM-powered path planning for robotic systems.

Thus, it is We can make very skillful use of various contacts desirable that a planning algorithm be robust to various (e.g., with the environment, our own body, etc.) to perform uncertainties like grasp center, extrinsic contact location, etc. complex manipulation. In a striking contrast, achieving such We present a method which can incorporate uncertainties in dexterous behavior for robots remains very challenging. Using several of the kinematic constraints to generate robust plans environmental contacts efficiently can provide additional for perform in-hand manipulation. This idea is also illustrated dexterity to robots while performing complex manipulation in Figure 1, where a naïve plan can easily lose contact with the [1]. However, the current generation of robotic systems environment due to uncertainty in the grasp location or the mostly avoid making contacts with their environment.

Efficient navigation through uneven terrain remains a challenging endeavor for autonomous robots. We propose a new geometric-based uneven terrain mapless navigation framework combining a Sparse Gaussian Process (SGP) local map with a Rapidly-Exploring Random Tree* (RRT*) planner. Our approach begins with the generation of a high-resolution SGP local map, providing an interpolated representation of the robot's immediate environment. This map captures crucial environmental variations, including height, uncertainties, and slope characteristics. Subsequently, we construct a traversability map based on the SGP representation to guide our planning process. The RRT* planner efficiently generates real-time navigation paths, avoiding untraversable terrain in pursuit of the goal. This combination of SGP-based terrain interpretation and RRT* planning enables ground robots to safely navigate environments with varying elevations and steep obstacles. We evaluate the performance of our proposed approach through robust simulation testing, highlighting its effectiveness in achieving safe and efficient navigation compared to existing methods.

Stackelberg planning is a recently introduced single-turn two-player adversarial planning model, where two players are acting in a joint classical planning task, the objective of the first player being hampering the second player from achieving its goal. This places the Stackelberg planning problem somewhere between classical planning and general combinatorial two-player games. But, where exactly? All investigations of Stackelberg planning so far focused on practical aspects. We close this gap by conducting the first theoretical complexity analysis of Stackelberg planning. We show that in general Stackelberg planning is actually no harder than classical planning. Under a polynomial plan-length restriction, however, Stackelberg planning is a level higher up in the polynomial complexity hierarchy, suggesting that compilations into classical planning come with a worst-case exponential plan-length increase. In attempts to identify tractable fragments, we further study its complexity under various planning task restrictions, showing that Stackelberg planning remains intractable where classical planning is not. We finally inspect the complexity of meta-operator verification, a problem that has been recently connected to Stackelberg planning.

Operating systems (OSes) are foundational to computer systems, managing hardware resources and ensuring secure environments for diverse applications. However, despite their enduring importance, the fundamental design objectives of OSes have seen minimal evolution over decades. Traditionally prioritizing aspects like speed, memory efficiency, security, and scalability, these objectives often overlook the crucial aspect of intelligence as well as personalized user experience. The lack of intelligence becomes increasingly critical amid technological revolutions, such as the remarkable advancements in machine learning (ML). Today's personal devices, evolving into intimate companions for users, pose unique challenges for traditional OSes like Linux and iOS, especially with the emergence of specialized hardware featuring heterogeneous components. Furthermore, the rise of large language models (LLMs) in ML has introduced transformative capabilities, reshaping user interactions and software development paradigms. While existing literature predominantly focuses on leveraging ML methods for system optimization or accelerating ML workloads, there is a significant gap in addressing personalized user experiences at the OS level. To tackle this challenge, this work proposes PerOS, a personalized OS ingrained with LLM capabilities. PerOS aims to provide tailored user experiences while safeguarding privacy and personal data through declarative interfaces, self-adaptive kernels, and secure data management in a scalable cloud-centric architecture; therein lies the main research question of this work: How can we develop intelligent, secure, and scalable OSes that deliver personalized experiences to thousands of users?

The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective in synthesizing safety-critical controllers. In this work, we propose a novel CBF-based local planner comprised of two components: Vessel and Mariner. The Vessel is a novel scaling factor based CBF formulation that synthesizes CBFs using only point cloud data. The Mariner is a CBF-based preview control framework that is used to mitigate getting stuck in spurious equilibria during navigation. To demonstrate the efficacy of our proposed approach, we first compare the proposed point cloud based CBF formulation with other point cloud based CBF formulations. Then, we demonstrate the performance of our proposed approach and its integration with global planners using experimental studies on the Unitree B1 and Unitree Go2 quadruped robots in various environments.

This paper investigates the problem of informative path planning for a mobile robotic sensor network in spatially temporally distributed mapping. The robots are able to gather noisy measurements from an area of interest during their movements to build a Gaussian Process (GP) model of a spatio-temporal field. The model is then utilized to predict the spatio-temporal phenomenon at different points of interest. To spatially and temporally navigate the group of robots so that they can optimally acquire maximal information gains while their connectivity is preserved, we propose a novel multistep prediction informative path planning optimization strategy employing our newly defined local cost functions. By using the dual decomposition method, it is feasible and practical to effectively solve the optimization problem in a distributed manner. The proposed method was validated through synthetic experiments utilizing real-world data sets.

Nano-drones, distinguished by their agility, minimal weight, and cost-effectiveness, are particularly well-suited for exploration in confined, cluttered and narrow spaces. Recognizing transparent, highly reflective or absorbing materials, such as glass and metallic surfaces is challenging, as classical sensors, such as cameras or laser rangers, often do not detect them. Inspired by bats, which can fly at high speeds in complete darkness with the help of ultrasound, this paper introduces \textit{BatDeck}, a pioneering sensor-deck employing a lightweight and low-power ultrasonic sensor for nano-drone autonomous navigation. This paper first provides insights about sensor characteristics, highlighting the influence of motor noise on the ultrasound readings, then it introduces the results of extensive experimental tests for obstacle avoidance (OA) in a diverse environment. Results show that \textit{BatDeck} allows exploration for a flight time of 8 minutes while covering 136m on average before crash in a challenging environment with transparent and reflective obstacles, proving the effectiveness of ultrasonic sensors for OA on nano-drones.

Current approaches for learning for planning have yet to achieve competitive performance against classical planners in several domains, and have poor overall performance. In this work, we construct novel graph representations of lifted planning tasks and use the WL algorithm to generate features from them. These features are used with classical machine learning methods which have up to 2 orders of magnitude fewer parameters and train up to 3 orders of magnitude faster than the state-of-the-art deep learning for planning models. Our novel approach, WL-GOOSE, reliably learns heuristics from scratch and outperforms the $h^{\text{FF}}$ heuristic in a fair competition setting. It also outperforms or ties with LAMA on 4 out of 10 domains on coverage and 7 out of 10 domains on plan quality. WL-GOOSE is the first learning for planning model which achieves these feats. Furthermore, we study the connections between our novel WL feature generation method, previous theoretically flavoured learning architectures, and Description Logic Features for planning.

Classical planning formulations like the Planning Domain Definition Language (PDDL) admit action sequences guaranteed to achieve a goal state given an initial state if any are possible. However, reasoning problems defined in PDDL do not capture temporal aspects of action taking, for example that two agents in the domain can execute an action simultaneously if postconditions of each do not interfere with preconditions of the other. A human expert can decompose a goal into largely independent constituent parts and assign each agent to one of these subgoals to take advantage of simultaneous actions for faster execution of plan steps, each using only single agent planning. By contrast, large language models (LLMs) used for directly inferring plan steps do not guarantee execution success, but do leverage commonsense reasoning to assemble action sequences. We combine the strengths of classical planning and LLMs by approximating human intuitions for two-agent planning goal decomposition. We demonstrate that LLM-based goal decomposition leads to faster planning times than solving multi-agent PDDL problems directly while simultaneously achieving fewer plan execution steps than a single agent plan alone and preserving execution success. Additionally, we find that LLM-based approximations of subgoals can achieve similar multi-agent execution steps than those specified by human experts. Website and resources at https://glamor-usc.github.io/twostep