DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
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| Authors | Hanwen Wang et al. |
| Year | 2026 |
| HF Upvotes | 20 |
| arXiv | 2605.16257 |
| Download | |
| Code | https://github.com/brave-eai/dexjoco |
Abstract
Achieving human-level manipulation requires dexterous robotic hands capable of complex object interactions. Advancing such capabilities further demands standardized benchmarks for systematic evaluation. However, existing dexterous benchmarks lack tasks that reflect the unique manipulation capabilities of dexterous hands over parallel grippers, as well as comprehensive evaluation pipelines. In this paper, we present DexJoCo, a benchmark and toolkit for task-oriented dexterous manipulation, comprising 11 functionally grounded tasks that evaluate tool-use, bimanual coordination, long-horizon execution, and reasoning. We develop a low-cost data collection system and collect 1.1K trajectories across these tasks, with support for domain randomization to assess robustness. We benchmark modern models under diverse settings, including visual and dynamics randomization, multi-task training, and action-head adaptation. Through extensive empirical analysis, we identify several important insights and common limitations of current policies in dexterous manipulation, highlighting key challenges for future research in dexterous hand robot learning. Project page available at: https://dexjoco.github.io
Engineering Breakdown
Plain English
DexJoCo is a benchmark suite with 11 robotic manipulation tasks designed to test dexterous hands (multi-fingered grippers) rather than simple parallel grippers. The authors collected 1.1K trajectories across tasks requiring tool-use, bimanual coordination, and long-horizon planning, built a low-cost data collection system, and validated the benchmark against modern learning approaches with domain randomization support for robustness testing.
Key Engineering Insight
The benchmark explicitly differentiates between capabilities unique to dexterous hands versus parallel grippers—this is critical because most existing benchmarks don't, meaning you can't actually measure whether your dexterous hand progress matters or if you're just building better parallel gripper simulators.
Why It Matters for Engineers
If you're building a production robotic system that needs to manipulate real objects (assembly, in-home tasks, repairs), you need standardized evaluation to know if investing in expensive dexterous hands pays off versus simpler alternatives. DexJoCo gives you a measurement baseline so you can make engineering tradeoff decisions with data instead of guesswork.
Research Context
Previous manipulation benchmarks (like standard MuJoCo tasks) didn't test capabilities that only dexterous hands unlock, so teams couldn't validate whether their hand hardware and control algorithms were actually solving harder problems. DexJoCo fills this gap by creating tasks where tool-use and in-hand manipulation become mandatory, enabling the field to measure real progress and compare algorithms fairly.
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