LIBERO-Industry: Benchmarking Robotic Manipulation under Industrial Process Constraints

Most simulation benchmarks for robotic manipulation keep target objects static and treat policy inference as instantaneous. These assumptions simplify evaluation but hide how a moving target evolves during computation and how stale an observation is when an action is executed. We introduce LIBERO-Dynamic, a dynamic extension of all 40 LIBERO tasks. It drives objects through contact friction along three trajectory families and speed tiers, while advancing the physical world according to calibrated, model-specific inference latency. The same mechanism supports training by replaying verified static demonstrations and compensating robot actions online for target displacement, producing 9,000 quality-filtered dynamic trajectories. We evaluate six pretrained policies on 1,800 dynamic conditions per configuration, compare execution strategies and action horizons with fixed checkpoints, and fine-tune three architectures using dynamic and mixed data. Faster motion degrades every policy but not uniformly; execution timing and horizon can change the behavior of the same policy; and asynchronous replanning benefits from frequent feedback only when the action queue avoids underflow. Dynamic demonstrations improve each architecture under at least one recipe, with gains varying by temporal conditioning, data composition, and training budget. A small-scale physical study shows task-dependent execution-strategy differences consistent with simulation. LIBERO-Dynamic makes object motion, inference latency, and action execution explicit and controllable for evaluating and improving robot policies in moving-target manipulation.

General-purpose agents can plan, use tools, and revise their behavior from feedback, but it remains unclear whether these capabilities transfer from digital environments to embodied manipulation. To investigate this question, we introduce LIBERO-Agent, an agent-native benchmark for evaluating these agents in robot manipulation tasks. Rather than asking agents to submit task-level Python control programs or operate through high-level robot skills, LIBERO-Agent provides an interactive robotic environment where agents can select which observations to inspect, process them with their own tools, and issue native action commands. LIBERO-Agent integrates 200 tasks into a common interaction framework and provides a 30-task primary suite that separates perception, short-horizon execution, and long-horizon composition. Results reveal a pronounced reliability gap: while agents perform well on perception and easy short-horizon tasks, their performance degrades substantially on hard short-horizon and long-horizon tasks. Richer observations improve short-horizon manipulation, while demonstration benefits depend on the agent and format. Among these agents, GPT-6 Astra achieves the strongest overall performance. Further analysis shows its major advantage lies in mechanism interaction, especially when sustained physical contact is needed, while its remaining failures stem from cross-stage interference and geometric errors.

Robot learning has undergone significant advancements in the domain of compliant control and robotic assembly. The development of novel methodologies has transcended the capacity to address discrete tasks, such as grasping or inserting, and has evolved to encompass the comprehensive construction of entire assemblies. Conventional benchmarks, which are predominantly focused on single tasks, are no longer adequate to address the complexity and interconnected nature of modern manufacturing processes. Consequently, the necessity for benchmarks to encompass a broader spectrum of assembly and subassembly steps is becoming apparent. In light of these developments, we propose LABIT, an industrial robotic assembly benchmark for long-horizon manipulation. This benchmark encompasses DIN-norm assembly tasks, including screwing, placing, inserting, and snapping, under both high and low tolerance conditions. The successive nature of these assembly steps implies that prior errors can influence subsequent manipulation. Consequently, this benchmark is particularly relevant for assessing the robustness of intelligent robotic systems. The proposed benchmarking protocols and novel performance metric, based on a taxonomy of assembly complexity in conjunction with the comprehensive list of parts and their corresponding CAD models, serve as essential tools for research and development in the field. To provide a baseline, we evaluate the benchmark using traditional force-position control and report reliability and score metrics per primitive, subtask, assembly category, and subassembly. Accompanying data is available at https://irp-hka.github.io/robotic-assembly-benchmark/.