When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution
In an era where artificial intelligence continues to permeate our daily lives, the demand for robots that can efficiently manage household tasks has never been higher. A new study titled “When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution,” recently published on arXiv, addresses a significant gap in the current capabilities of embodied AI systems. Researchers have introduced a novel benchmark called LongAct, aimed at enhancing the planning and reasoning abilities necessary for executing complex, long-term household tasks.
Traditional embodied AI benchmarks tend to focus on short-horizon navigation or manipulation, often relying on fixed task categories that limit the scope of evaluation. LongAct, however, emphasizes the importance of high-level planning autonomy in long-horizon tasks, which are typically more intricate and require advanced cognitive skills. This benchmark is designed to assess how well AI agents can understand free-form instructions, manage dependencies, maintain memory, and adapt their plans as circumstances change.
Key Features of LongAct
- High-Level Cognitive Capabilities: LongAct isolates essential skills such as instruction comprehension, dependency management, and adaptive planning, allowing for a more accurate assessment of an agent’s performance.
- Emphasis on Free-Form Instructions: By utilizing natural language inputs, LongAct evaluates how well agents can interpret and execute complex household tasks without predefined categories.
- Focus on Long-Horizon Tasks: Unlike previous benchmarks, LongAct challenges agents to maintain performance over extended periods, simulating real-world scenarios where tasks may not be completed in a single session.
To complement the LongAct benchmark, researchers have developed HoloMind, an advanced AI agent that leverages a variety of sophisticated technologies. HoloMind features a Directed Acyclic Graph (DAG)-based hierarchical planner that enables it to strategize effectively over long time frames. Additionally, it incorporates a Multimodal Spatial Memory system for persistent world modeling, an Episodic Memory for experience reuse, and a global Critic for reflective supervision, all of which contribute to its enhanced performance.
Experimental Results
In a series of experiments utilizing cutting-edge models such as GPT-5 and Qwen3-VL, researchers discovered that HoloMind significantly improves long-horizon performance in household task execution. Despite these advancements, even the most sophisticated models achieved only 59% goal completion and 16% full-task success. These findings underscore the inherent challenges associated with long-horizon task planning and execution in embodied AI agents.
The results highlight the pressing need for continued research and development in this area. While current models show promise, the benchmarks established by LongAct reveal that achieving high-level planning autonomy in real-world scenarios remains a formidable challenge. As household robots become increasingly integrated into our lives, enhancing their capabilities through rigorous benchmarks like LongAct will be crucial for their success.
Conclusion
The introduction of LongAct and HoloMind marks a significant step forward in the quest to create more autonomous and capable robotic agents. By addressing the complexities of long-horizon tasks and emphasizing advanced cognitive processes, researchers are paving the way for a future where robots can effectively manage household chores, improving convenience and efficiency in our daily lives.
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