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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.03269 |
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| _version_ | 1866918485742845952 |
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| author | Kim, Dongyoung Jang, Huiwon Koo, Myungkyu Jang, Suhyeok Kim, Taeyoung Kim, Beomjun Yoon, Byungjun Jang, Changsung Choi, Daewon Han, Dongsu Lee, Donguk Kwon, Heeseung Jeon, Hojin Kang, Jaehyun Bae, Jaekyoung Lee, Jihyuk Lee, Jimin Won, John Ahn, Joonwoo Park, Junhyeong Sung, Junyoung Lee, Kyungmin Han, Minseong Yoon, Minsung Joo, Sejune Son, Seonil Park, Seungcheol Cho, Seunggeun Moon, Seungjun Kim, Seungku Dong, Yonghoon Cho, Yongjin Kim, Youngchan Kim, Chang Hwan Kim, Dohyeon Kim, Heecheol Lee, Heewon Ahn, Hensen Ryu, Hyungkyu Choi, Hyunsoo Shin, Hyunsoo Jung, Jaeheon Kim, Jaewoo Kim, Jinwook Chang, Joochul Kim, Joonsoo Park, Junghun Park, Jungwoo Cho, Junho Park, Junhyeok Lee, Junwon Lee, Kangwook Kim, Kwanghoon Choe, Kyoungwhan Bhadu, Manoj Oh, Nayoung Kim, Sangjun Kim, Sangwoo Shim, Seunghoon Kim, Seunghyun Lee, Seungjun Ka, Seungyup Yang, Sungryol Jung, Wook Shukla, Yashu Lee, Yeonjae Bae, Yeonwoo Shin, Jinwoo |
| author_facet | Kim, Dongyoung Jang, Huiwon Koo, Myungkyu Jang, Suhyeok Kim, Taeyoung Kim, Beomjun Yoon, Byungjun Jang, Changsung Choi, Daewon Han, Dongsu Lee, Donguk Kwon, Heeseung Jeon, Hojin Kang, Jaehyun Bae, Jaekyoung Lee, Jihyuk Lee, Jimin Won, John Ahn, Joonwoo Park, Junhyeong Sung, Junyoung Lee, Kyungmin Han, Minseong Yoon, Minsung Joo, Sejune Son, Seonil Park, Seungcheol Cho, Seunggeun Moon, Seungjun Kim, Seungku Dong, Yonghoon Cho, Yongjin Kim, Youngchan Kim, Chang Hwan Kim, Dohyeon Kim, Heecheol Lee, Heewon Ahn, Hensen Ryu, Hyungkyu Choi, Hyunsoo Shin, Hyunsoo Jung, Jaeheon Kim, Jaewoo Kim, Jinwook Chang, Joochul Kim, Joonsoo Park, Junghun Park, Jungwoo Cho, Junho Park, Junhyeok Lee, Junwon Lee, Kangwook Kim, Kwanghoon Choe, Kyoungwhan Bhadu, Manoj Oh, Nayoung Kim, Sangjun Kim, Sangwoo Shim, Seunghoon Kim, Seunghyun Lee, Seungjun Ka, Seungyup Yang, Sungryol Jung, Wook Shukla, Yashu Lee, Yeonjae Bae, Yeonwoo Shin, Jinwoo |
| contents | While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, long-term memory, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including data synthesis for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. $π_{0.5}$ and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while $π_{0.5}$ and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03269 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RLDX-1 Technical Report Kim, Dongyoung Jang, Huiwon Koo, Myungkyu Jang, Suhyeok Kim, Taeyoung Kim, Beomjun Yoon, Byungjun Jang, Changsung Choi, Daewon Han, Dongsu Lee, Donguk Kwon, Heeseung Jeon, Hojin Kang, Jaehyun Bae, Jaekyoung Lee, Jihyuk Lee, Jimin Won, John Ahn, Joonwoo Park, Junhyeong Sung, Junyoung Lee, Kyungmin Han, Minseong Yoon, Minsung Joo, Sejune Son, Seonil Park, Seungcheol Cho, Seunggeun Moon, Seungjun Kim, Seungku Dong, Yonghoon Cho, Yongjin Kim, Youngchan Kim, Chang Hwan Kim, Dohyeon Kim, Heecheol Lee, Heewon Ahn, Hensen Ryu, Hyungkyu Choi, Hyunsoo Shin, Hyunsoo Jung, Jaeheon Kim, Jaewoo Kim, Jinwook Chang, Joochul Kim, Joonsoo Park, Junghun Park, Jungwoo Cho, Junho Park, Junhyeok Lee, Junwon Lee, Kangwook Kim, Kwanghoon Choe, Kyoungwhan Bhadu, Manoj Oh, Nayoung Kim, Sangjun Kim, Sangwoo Shim, Seunghoon Kim, Seunghyun Lee, Seungjun Ka, Seungyup Yang, Sungryol Jung, Wook Shukla, Yashu Lee, Yeonjae Bae, Yeonwoo Shin, Jinwoo Robotics Artificial Intelligence Machine Learning While Vision-Language-Action models (VLAs) have shown remarkable progress toward human-like generalist robotic policies through the versatile intelligence (i.e. broad scene understanding and language-conditioned generalization) inherited from pre-trained Vision-Language Models, they still struggle with complex real-world tasks requiring broader functional capabilities (e.g. motion awareness, long-term memory, and physical sensing). To address this, we introduce RLDX-1, a general-purpose robotic policy for dexterous manipulation built on the Multi-Stream Action Transformer (MSAT), an architecture that unifies these capabilities by integrating heterogeneous modalities through modality-specific streams with cross-modal joint self-attention. RLDX-1 further combines this architecture with system-level design choices, including data synthesis for rare manipulation scenarios, learning procedures specialized for human-like manipulation, and inference optimizations for real-time deployment. Through empirical evaluation, we show that RLDX-1 consistently outperforms recent frontier VLAs (e.g. $π_{0.5}$ and GR00T N1.6) across both simulation benchmarks and real-world tasks that require broad functional capabilities beyond general versatility. In particular, RLDX-1 shows superiority in ALLEX humanoid tasks by achieving success rates of 86.8% while $π_{0.5}$ and GR00T N1.6 achieve around 40%, highlighting the ability of RLDX-1 to control a high-DoF humanoid robot under diverse functional demands. Together, these results position RLDX-1 as a promising step toward reliable VLAs for complex, contact-rich, and dynamic real-world dexterous manipulation. |
| title | RLDX-1 Technical Report |
| topic | Robotics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.03269 |