Robotic planning and execution in open-world environments is a complex problem due to the vast state spaces and high variability of task embodiment. Recent advances in perception algorithms, combined with Large Language Models (LLMs) for planning, offer promising solutions to these challenges, as the common sense reasoning capabilities of LLMs provide a strong heuristic for efficiently searching the action space. However, prior work fails to address the possibility of hallucinations from LLMs, which results in failures to execute the planned actions largely due to logical fallacies at high- or low-levels. To contend with automation failure due to such hallucinations, we introduce ConceptAgent, a natural language-driven robotic platform designed for task execution in unstructured environments. With a focus on scalability and reliability of LLM-based planning in complex state and action spaces, we present innovations designed to limit these shortcomings, including 1) Predicate Grounding to prevent and recover from infeasible actions, and 2) an embodied version of LLM-guided Monte Carlo Tree Search with self reflection. In simulation experiments, ConceptAgent achieved a 19% task completion rate across three room layouts and 30 easy level embodied tasks outperforming other state-of-the-art LLM-driven reasoning baselines that scored 10.26% and 8.11% on the same benchmark. Additionally, ablation studies on moderate to hard embodied tasks revealed a 20% increase in task completion from the baseline agent to the fully enhanced ConceptAgent, highlighting the individual and combined contributions of Predicate Grounding and LLM-guided Tree Search to enable more robust automation in complex state and action spaces.

In this work, we propose an end-to-end Thrust Microstepping and Decoupled Control (TMDC) of quadrotors. TMDC focuses on precise off-centered aerial grasping of payloads dynamically, which are attached rigidly to the UAV body via a gripper contrary to the swinging payload. The dynamic payload grasping quickly changes UAV's mass, inertia etc, causing instability while performing a grasping operation in-air. We identify that to handle unknown payload grasping, the role of thrust controller is crucial. Hence, we focus on thrust control without involving system parameters such as mass etc. TMDC is based on our novel Thrust Microstepping via Acceleration Feedback (TMAF) thrust controller and Decoupled Motion Control (DMC). TMAF precisely estimates the desired thrust even at smaller loop rates while DMC decouples the horizontal and vertical motion to counteract disturbances in the case of dynamic payloads. We prove the controller's efficacy via exhaustive experiments in practically interesting and adverse real-world cases, such as fully onboard state estimation without any positioning sensor, narrow and indoor flying workspaces with intense wind turbulence, heavy payloads, non-uniform loop rates, etc. Our TMDC outperforms recent direct acceleration feedback thrust controller (DA) and geometric tracking control (GT) in flying stably for aerial grasping and achieves RMSE below 0.04m in contrast to 0.15m of DA and 0.16m of GT.

This paper introduces an innovative approach to analyzing and improving multi-hop reasoning in AI systems by drawing inspiration from Hamiltonian mechanics. We propose a novel framework that maps reasoning chains in embedding spaces to Hamiltonian systems, allowing us to leverage powerful analytical tools from classical physics. Our method defines a Hamiltonian function that balances the progression of reasoning (kinetic energy) against the relevance to the question at hand (potential energy). Using this framework, we analyze a large dataset of reasoning chains from a multi-hop question-answering task, revealing intriguing patterns that distinguish valid from invalid reasoning. We show that valid reasoning chains have lower Hamiltonian energy and move in ways that make the best trade-off between getting more information and answering the right question. Furthermore, we demonstrate the application of this framework to steer the creation of more efficient reasoning algorithms within AI systems. Our results not only provide new insights into the nature of valid reasoning but also open up exciting possibilities for physics-inspired approaches to understanding and improving artificial intelligence.

In recent decades, there has been substantial advances in time series models and benchmarks across various individual tasks, such as time series forecasting, classification, and anomaly detection. Meanwhile, compositional reasoning in time series is prevalent in real-world applications (e.g., decision-making and compositional question answering) and is in great demand. Unlike simple tasks that primarily focus on predictive accuracy, compositional reasoning emphasizes the synthesis of diverse information from both time series data and various domain knowledge, making it distinct and extremely more challenging. In this paper, we introduce Compositional Time Series Reasoning, a new task of handling intricate multistep reasoning tasks from time series data. Specifically, this new task focuses on various question instances requiring structural and compositional reasoning abilities on time series data, such as decision-making and compositional question answering. As an initial attempt to tackle this novel task, we developed TS-Reasoner, a program-aided approach that utilizes large language model (LLM) to decompose a complex task into steps of programs that leverage existing time series models and numerical subroutines. Unlike existing reasoning work which only calls off-the-shelf modules, TS-Reasoner allows for the creation of custom modules and provides greater flexibility to incorporate domain knowledge as well as user-specified constraints. We demonstrate the effectiveness of our method through a comprehensive set of experiments. These promising results indicate potential opportunities in the new task of time series reasoning and highlight the need for further research.

In this paper, the authors propose to improve exploration in deep RL algorithms by adding a distance term into the loss function. They show that adding this term provides better results that not doing so. After rebuttal: The authors did a much better job explaining their work in the rebuttal, so I'm now convinced that they have a contribution. I'm now more inclined in favor of this paper, but the authors will have to explain much more carefully what they are doing (included a better presentation of the formalism) and how it is positionned with respect to the literature. I keep the rest of the review as it was.

Review of submission 1489: EX2: Exploration with Exemplar Models for Deep Reinforcement Learning Summary: A discriminative novelty detection algorithm is proposed to improve exploration for policy gradient based reinforcement learning algorithms. The implicitly-estimated density by the discriminative novelty detection of a state is then used to produce a reward bonus added to the original reward for down-stream policy optimization algorithms (TRPO). Two techniques are discussed to improve the computation efficiency. Comments - One motivation of the paper is to utilize implicit density estimation to approximate classic count based exploration. The discriminative novelty detection only maintains a density estimation over the states, but not state-action pairs.

We may earn revenue from the products available on this page and participate in affiliate programs. Building a robot at home is more than just a fun activity--it's a hands-on way to explore the exciting world of STEM [Science, Technology, Engineering, and Math]. Whether you're searching for a children's toy robot to inspire curiosity or a more advanced robot-building kit for older kids or teens, like our best overall Sillbird STEM 12-in-1 Education Solar Robot Toy, the best robot kits offer options for all ages and skill levels. Robot building kits offer a perfect blend of creativity and learning, teaching essential skills like coding, problem-solving, and engineering through play. From preschool-friendly robot toys to beginner robotics kits for older children, these sets provide a fantastic introduction to the basics of robotics.

Footage circulating on social media appeared to show smoke rising over the Feodosia terminal. Local Russian-installed officials told RIA Novosti that efforts to extinguish the fire were ongoing. Meanwhile, the defence ministry in Moscow said that 12 Ukrainian drones were shot down over the peninsula overnight out of a total of 21 launched by Kyiv. In a statement announcing the attack, Ukraine's general staff said that oil products shipped from the terminal were being used to "meet the needs of the Russian occupation army". The facility was previously hit in a Ukrainian drone strike in March.

I have increased my score rom a 4 to a 7. The main concern in my original review was that the reward signal was switched from dense to sparse rewards for evaluation but I am now convinced that it's a reasonable domain for analysis. I think an explicit discussion on switching the reward signal would be useful to include in the final version of the paper. They show how to use MAML to update the distribution of the latent state in addition to using standard MAML to update the parameters of the policy. They empirically show that these policies learn a reasonable exploration policy in sparse-reward manipulation and locomation domains. I like the semantics of using MAML to update the distribution of a policy's stochastic latent state.

This is an excellent theoretical contribution. The analysis is quite heavy and has many subtleties. I do not have enough time to read the appended proofs; also, the subject of the paper is not in my area of research. The comments below are based on the impression I got after reading carefully the first 8 pages of the paper and glancing through the rest in the supplementary file. Summary: This paper is about reinforcement learning in weakly-communicating MDP under the average-reward criterion.