We compared RAPS with the latest state-of-the-art work that incorporates DMPs with Deep RL: Neural Dynamic Policies [6]. One question that may arise is: How useful isthe dummy primitive? We runanexperiment with and without thedummy primitiveinorder toevaluate itsimpact, and find that the dummy primitive improves performance significantly. Each image depicts the solution of one of the tasks, we omit the bottom burner task as it is the goal is the same as the top burner task, just with a different dial to turn. For the sequential multi-task version of the environment, in a single episode, the goal is to complete four different subtasks.

Efficiently leveraging simulation to acquire advanced manipulation skills is both challenging and highly significant. We introduce \textit{ForeRobo}, a generative robotic agent that utilizes generative simulations to autonomously acquire manipulation skills driven by envisioned goal states. Instead of directly learning low-level policies, we advocate integrating generative paradigms with classical control. Our approach equips a robotic agent with a self-guided \textit{propose-generate-learn-actuate} cycle. The agent first proposes the skills to be acquired and constructs the corresponding simulation environments; it then configures objects into appropriate arrangements to generate skill-consistent goal states (\textit{ForeGen}). Subsequently, the virtually infinite data produced by ForeGen are used to train the proposed state generation model (\textit{ForeFormer}), which establishes point-wise correspondences by predicting the 3D goal position of every point in the current state, based on the scene state and task instructions. Finally, classical control algorithms are employed to drive the robot in real-world environments to execute actions based on the envisioned goal states. Compared with end-to-end policy learning methods, ForeFormer offers superior interpretability and execution efficiency. We train and benchmark ForeFormer across a variety of rigid-body and articulated-object manipulation tasks, and observe an average improvement of 56.32\% over the state-of-the-art state generation models, demonstrating strong generality across different manipulation patterns. Moreover, in real-world evaluations involving more than 20 robotic tasks, ForeRobo achieves zero-shot sim-to-real transfer and exhibits remarkable generalization capabilities, attaining an average success rate of 79.28\%.

New York's new mayor Zohran Mamdani tells Trump'I have four words for you' in blistering victory speech quoting his socialist hero, bragging about'toppling a dynasty' and promising a'new dawn' This Leftist election landslide was caused by the same vile disease that's triggered a GOP civil war. Why Mamdani's socialist revolution in New York has sparked a civil war for Democrats... and Trump is secretly loving it Simone Biles details all the plastic surgery she's had after her boob job this summer Hollywood A-listers may be blacklisted for'antisemitism' under Paramount's new anti-woke leadership Prince Harry issues defiant statement as he denies claims he was trying to upstage William by announcing pseudo-royal Canada trip at same time as his brother's five-day tour of Brazil Inside Kate and William's forever home: Princess is kitting out Forest Lodge in her preferred'classic contemporary style' to create a'lovely but absolutely inoffensive' look REVEALED: Fattest states in America ranked... including region where three-quarters of residents are obese I was so desperate for a baby I stole sperm from my husband's condom: It's the most shocking confession. Now for the first time LIZ JONES tells what happened next... and the consequence no one saw Texas teen'tears masterpiece from wall at the Met in unhinged meltdown' before being handed in by his MOTHER Amazon signals it's finally fed up with Whole Foods' sluggish sales - and is making sweeping, controversial changes READ MORE: Was IKEA right about the kitchen of 2025? The beloved kettle is among the kitchen appliances that will have vanished in 50 years' time thanks to Gen Z, new research suggests. Today's youngsters will fuel the move away from bulky, wired kitchen appliances favoured by older generations, according to experts.

We introduce Behavior from Language and Demonstration (BLADE), a framework for long-horizon robotic manipulation by integrating imitation learning and model-based planning. BLADE leverages language-annotated demonstrations, extracts abstract action knowledge from large language models (LLMs), and constructs a library of structured, high-level action representations. These representations include preconditions and effects grounded in visual perception for each high-level action, along with corresponding controllers implemented as neural network-based policies. BLADE can recover such structured representations automatically, without manually labeled states or symbolic definitions. BLADE shows significant capabilities in generalizing to novel situations, including novel initial states, external state perturbations, and novel goals. We validate the effectiveness of our approach both in simulation and on real robots with a diverse set of objects with articulated parts, partial observability, and geometric constraints.

Large Language Models (LLMs) are increasingly used for decision making in embodied agents, yet existing safety evaluations often rely on coarse success rates and domain-specific setups, making it difficult to diagnose why and where these models fail. This obscures our understanding of embodied safety and limits the selective deployment of LLMs in high-risk physical environments. We introduce SAFEL, the framework for systematically evaluating the physical safety of LLMs in embodied decision making. SAFEL assesses two key competencies: (1) rejecting unsafe commands via the Command Refusal Test, and (2) generating safe and executable plans via the Plan Safety Test. Critically, the latter is decomposed into functional modules, goal interpretation, transition modeling, action sequencing, enabling fine-grained diagnosis of safety failures. To support this framework, we introduce EMBODYGUARD, a PDDL-grounded benchmark containing 942 LLM-generated scenarios covering both overtly malicious and contextually hazardous instructions. Evaluation across 13 state-of-the-art LLMs reveals that while models often reject clearly unsafe commands, they struggle to anticipate and mitigate subtle, situational risks. Our results highlight critical limitations in current LLMs and provide a foundation for more targeted, modular improvements in safe embodied reasoning.

From making tea to cleaning the floors, everyday life often feels like one huge chore. But the opportunity to offload such menial tasks to your own personal robot helper may arrive sooner than you think. In a promo clip, the advanced humanoid boils the kettle, vacuums floors, carries groceries, cleans windows and puts up a picture frame. At the end of the video, it takes a well-earned sit in the longue – while its blissfully-happy owners drink wine in the next room. Although it is currently a prototype, the creation could be autonomously completing chores in customers' homes by the end of the decade.

This paper proposes a framework for assessing the novelty of design problems using the SAPPhIRE model of causality. SAPPhIRE denotes different abstraction levels where S stands for State change, A stands for Action, P stands for Parts, Ph stands for Physical Phenomena, I stands for Input, R stands for oRgan and E stands for Physical Effect. The novelty of a problem is measured as its minimum distance from the problems in a reference problem database. The distance is calculated by comparing the current problem and each reference past problem at the various levels of abstraction in the SAPPhIRE ontology. The basis for comparison is textual similarity. To demonstrate the applicability of the proposed framework, The'current' set of problems associated with an artifact, as collected from its stakeholders, were compared with the'past' set of problems, as collected from patents and other web sources, to assess the novelty of the'current' set. This approach is aimed at providing a better understanding of the degree of novelty of any given set of current problems by comparing them to similar problems available from historical records. By applying such approaches, organizations could effectively prioritize and address emerging problems based on their relative novelty, with positive ramifications on problem-solving and decision-making. Since manual assessment, the current mode of such assessments as reported in the literature, is a tedious process, to reduce time complexity and to afford better applicability for larger sets of problem statements, an automated assessment is proposed and used in this paper.

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Advancements in large language models (LLMs) have demonstrated their potential in facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs have also been able to generate reward functions for low-level robot actions, effectively bridging the interface between high-level planning and low-level robot control. However, the challenge remains that even with syntactically correct plans, robots can still fail to achieve their intended goals. This failure can be attributed to imperfect plans proposed by LLMs or to unforeseeable environmental circumstances that hinder the execution of planned subtasks due to erroneous assumptions about the state of objects. One way to prevent these challenges is to rely on human-provided step-by-step instructions, limiting the autonomy of robotic systems. Vision Language Models (VLMs) have shown remarkable success in tasks such as visual question answering and image captioning. Leveraging the capabilities of VLMs, we present a novel framework called Robotic Replanning with Perception and Language Models (RePLan) that enables real-time replanning capabilities for long-horizon tasks. This framework utilizes the physical grounding provided by a VLM's understanding of the world's state to adapt robot actions when the initial plan fails to achieve the desired goal. We test our approach within four environments containing seven long-horizion tasks. We find that RePLan enables a robot to successfully adapt to unforeseen obstacles while accomplishing open-ended, long-horizon goals, where baseline models cannot. Find more information at https://replan-lm.github.io/replan.github.io/