diff --git a/template.yaml b/template.yaml
index 42afd77..66fe5eb 100644
--- a/template.yaml
+++ b/template.yaml
@@ -1,209 +1,199 @@
organization: OMRON SINIC X
twitter: '@omron_sinicx'
-title: 'MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics'
-conference: IJCAI2020
+title: 'Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System'
+conference: IROS2024
resources:
- paper: https://arxiv.org/abs/1909.13111
- code: https://github.com/omron-sinicx/multipolar
- video: https://www.youtube.com/embed/adUnIj83RtU
- blog: https://medium.com/sinicx/multipolar-multi-source-policy-aggregation-for-transfer-reinforcement-learning-between-diverse-bc42a152b0f5
-description: explore a new challenge in transfer RL, where only a set of source policies collected under unknown diverse dynamics is available for learning a target task efficiently.
-image: https://omron-sinicx.github.io/multipolar/assets/teaser.png
-url: https://omron-sinicx.github.io/multipolar
-speakerdeck: b7a0614c24014dcbbb121fbb9ed234cd
+ paper: http://arxiv.org/abs/2406.14990
+ # code: https://github.com/omron-sinicx/multipolar
+ video: https://youtu.be/qejVTnmWFdo
+ # blog: https://medium.com/sinicx/multipolar-multi-source-policy-aggregation-for-transfer-reinforcement-learning-between-diverse-bc42a152b0f5
+description: "CompACT: Learning Compliance Control via Action Chunking with Transformers. (Code to be released soon)"
+image: https://omron-sinicx.github.io/CompACT/assets/overview.png
+url: https://omron-sinicx.github.io/CompACT
+# speakerdeck: b7a0614c24014dcbbb121fbb9ed234cd
authors:
- - name: Mohammadamin Barekatain*
+ - name: Tatsuya Kamijo*+
affiliation: [1, 2]
- url: http://barekatain.me/
position: intern
- - name: Ryo Yonetani
+ - name: Cristian C. Beltran-Hernandez*
affiliation: [1]
- position: principal investigator
- url: https://yonetaniryo.github.io/
+ position: senior researcher
+ url: https://cristianbehe.me/
- name: Masashi Hamaya
affiliation: [1]
- position: senior researcher
+ position: principal investigator
url: https://sites.google.com/view/masashihamaya/home
- # - name: Mai Nishimura
- # affiliation: [1]
- # url: https://denkiwakame.github.io
- # - name: Asako Kanezaki
- # affiliation: [2]
- # url: https://kanezaki.github.io/
contact_ids: ['github', 'omron', 2] #=> github issues, contact@sinicx.com, 2nd author
affiliations:
- OMRON SINIC X Corporation
- - Technical University of Munich
+ - University of Tokyo
meta:
- - '* work done as an intern at OMRON SINIC X.'
+ - '* Equal Contribution'
+extra:
+ - '+ work done as an intern at OMRON SINIC X.'
bibtex: >
# arXiv version
- @article{barekatain2019multipolar,
- title={MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics},
- author={Barekatain, Mohammadamin and Yonetani, Ryo and Hamaya, Masashi},
- journal={arXiv preprint arXiv:1909.13111},
- year={2019}
- }
-
- # IJCAI version
-
- @inproceedings{barekatain2020multipolar,
- title={MULTIPOLAR: Multi-Source Policy Aggregation for Transfer Reinforcement Learning between Diverse Environmental Dynamics},
- author={Barekatain, Mohammadamin and Yonetani, Ryo and Hamaya, Masashi},
- booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
- year={2020}
+ @article{kamijo2024learning,
+ title={Learning Variable Compliance Control From a Few Demonstrations for Bimanual Robot with Haptic Feedback Teleoperation System},
+ author={Kamijo, Tatsuya and Beltran-Hernandez, Cristian C and Hamaya, Masashi},
+ journal={arXiv preprint arXiv:2406.14990},
+ year={2024}
}
-teaser: teaser.png
+teaser: overview.png
overview: |
- Transfer reinforcement learning (RL) aims at improving learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.
- However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments.
- In this work, we explore a new challenge in transfer RL, where only a set of source policies collected under unknown diverse dynamics is available for learning a target task efficiently.
- To address this problem, the proposed approach, **MULTI-source POLicy AggRegation (MULTIPOLAR)**, comprises two key techniques.
- We learn to aggregate the actions provided by the source policies adaptively to maximize the target task performance.
- Meanwhile, we learn an auxiliary network that predicts residuals around the aggregated actions, which ensures the target policy”s expressiveness even when some of the source policies perform poorly.
- We demonstrated the effectiveness of MULTIPOLAR through an extensive experimental evaluation across six simulated environments ranging from classic control problems to challenging robotics simulations, under both continuous and discrete action spaces.
+ Automating dexterous, contact-rich manipulation tasks using rigid robots is a significant challenge in robotics. Rigid robots, defined by
+ their actuation through position commands, face issues of excessive contact forces due to their inability to adapt to contact with the environment,
+ potentially causing damage. While compliance control schemes have been introduced to mitigate these issues by controlling forces via external sensors,
+ they are hampered by the need for fine-tuning task-specific controller parameters. Learning from Demonstrations (LfD) offers an intuitive alternative,
+ allowing robots to learn manipulations through observed actions. In this work, we introduce a novel system to enhance the teaching of dexterous,
+ contact-rich manipulations to rigid robots. Our system is twofold: firstly, it incorporates a teleoperation interface utilizing Virtual Reality (VR)
+ controllers, designed to provide an intuitive and cost-effective method for task demonstration with haptic feedback. Secondly, we present Comp-ACT
+ (Compliance Control via Action Chunking with Transformers), a method that leverages the demonstrations to learn variable compliance control from a few
+ demonstrations. Our methods have been validated across various complex contact-rich manipulation tasks using single-arm and bimanual robot setups in
+ simulated and real-world environments, demonstrating the effectiveness of our system in teaching robots dexterous manipulations with enhanced adaptability
+ and safety.
-body:
- - title: subsection 1
- image: method.png
- text: >
- **test text with unicode characters:** α, β, φ, ψ
- - title: subsection 2
- image: null
- text: >
- **test text with TeX characters:** $\alpha$, $\beta$, $\phi$, $\psi \\$
- see how it renders with $\KaTeX$.
- $$ E = mc^2$$
- $$ \int \oint \sum \prod $$
- $$ \begin{CD} A @>a>> B \\ @VbVV @AAcA \\ C @= D \end{CD} $$
- - title: null
- image: method.png
- text: >
- This is a multi-line text example.
- "> - Flow Style" converts newlines to spaces.
- Using >, newline characters are converted to spaces.
- Newline characters and indentation are handled appropriately, and the text is represented as a single line.
- It's suitable when you want to collapse multi-line text into a single line, such as in configurations or descriptions where readability is key.
- - text: |
- This is a multi-line
- text example.
- "| - Block Style" preserves newlines and indentation.
- Using |, you can represent multi-line text that includes newline characters.
- Newline characters are preserved exactly as they are, along with the block's indentation.
- It's suitable when maintaining newlines and indentation is important, such as preserving the structure of code or prose.
- - title: Results
- text: |
- ### Motion Planning (MP) Dataset
- markdown version
- |Method|Opt|Exp|Hmean|
- |--|--|--|--|
- |BF| 65.8 (63.8, 68.0)| 44.1 (42.8, 45.5) | 44.8 (43.4, 46.3)|
- |WA*| 68.4 (66.5, 70.4)| 35.8 (34.5, 37.1) | 40.4 (39.0, 41.8)|
- |**Neural A*** | **87.7 (86.6, 88.9)**| 40.1 (38.9, 41.3) | 52.0 (50.7, 53.3)|
+# body:
+# - title: subsection 1
+# image: method.png
+# text: >
+# **test text with unicode characters:** α, β, φ, ψ
+# - title: subsection 2
+# image: null
+# text: >
+# **test text with TeX characters:** $\alpha$, $\beta$, $\phi$, $\psi \\$
+# see how it renders with $\KaTeX$.
+# $$ E = mc^2$$
+# $$ \int \oint \sum \prod $$
+# $$ \begin{CD} A @>a>> B \\ @VbVV @AAcA \\ C @= D \end{CD} $$
+# - title: null
+# image: method.png
+# text: >
+# This is a multi-line text example.
+# "> - Flow Style" converts newlines to spaces.
+# Using >, newline characters are converted to spaces.
+# Newline characters and indentation are handled appropriately, and the text is represented as a single line.
+# It's suitable when you want to collapse multi-line text into a single line, such as in configurations or descriptions where readability is key.
+# - text: |
+# This is a multi-line
+# text example.
+# "| - Block Style" preserves newlines and indentation.
+# Using |, you can represent multi-line text that includes newline characters.
+# Newline characters are preserved exactly as they are, along with the block's indentation.
+# It's suitable when maintaining newlines and indentation is important, such as preserving the structure of code or prose.
+# - title: Results
+# text: |
+# ### Motion Planning (MP) Dataset
+# markdown version
+# |Method|Opt|Exp|Hmean|
+# |--|--|--|--|
+# |BF| 65.8 (63.8, 68.0)| 44.1 (42.8, 45.5) | 44.8 (43.4, 46.3)|
+# |WA*| 68.4 (66.5, 70.4)| 35.8 (34.5, 37.1) | 40.4 (39.0, 41.8)|
+# |**Neural A*** | **87.7 (86.6, 88.9)**| 40.1 (38.9, 41.3) | 52.0 (50.7, 53.3)|
- 
- 
+#
+#
Motion Planning (MP) Dataset
-HTML version
-
-
-
-
-
- | Method | -Opt | -Exp | -Hmean | -
|---|---|---|---|
|
- BF
- - WA* - |
-
- 65.8 (63.8, 68.0)
- - 68.4 (66.5, 70.4) - |
-
- 44.1 (42.8, 45.5)
- - 35.8 (34.5, 37.1) - |
-
- 44.8 (43.4, 46.3)
- - 40.4 (39.0, 41.8) - |
-
|
- SAIL
- - SAIL-SL - - BB-A* - |
-
- 5.7 (4.6, 6.8)
- - 3.1 (2.3, 3.8) - - 31.2 (28.8, 33.5) - |
-
- 58.0 (56.1, 60.0)
- - 57.6 (55.7, 59.6) - - 52.0 (50.2, 53.9) - |
-
- 7.7 (6.4, 9.0)
- - 4.4 (3.5, 5.3) - - 31.1 (29.2, 33.0) - |
-
|
- Neural BF
- - Neural A* - |
-
- 75.5 (73.8, 77.1)
- - 87.7 (86.6, 88.9) - |
-
- 45.9 (44.6, 47.2)
- - 40.1 (38.9, 41.3) - |
-
- 52.0 (50.7, 53.4)
- - 52.0 (50.7, 53.3) - |
-
Selected Path Planning Results
-dummy text
-
- Path Planning Results on SSD Dataset
-dummy text
-
+# Motion Planning (MP) Dataset
+#HTML version
+#
+#
+#
+#
+#
+# | Method | +#Opt | +#Exp | +#Hmean | +#
|---|---|---|---|
|
+# BF
+# +# WA* +# |
+#
+# 65.8 (63.8, 68.0)
+# +# 68.4 (66.5, 70.4) +# |
+#
+# 44.1 (42.8, 45.5)
+# +# 35.8 (34.5, 37.1) +# |
+#
+# 44.8 (43.4, 46.3)
+# +# 40.4 (39.0, 41.8) +# |
+#
|
+# SAIL
+# +# SAIL-SL +# +# BB-A* +# |
+#
+# 5.7 (4.6, 6.8)
+# +# 3.1 (2.3, 3.8) +# +# 31.2 (28.8, 33.5) +# |
+#
+# 58.0 (56.1, 60.0)
+# +# 57.6 (55.7, 59.6) +# +# 52.0 (50.2, 53.9) +# |
+#
+# 7.7 (6.4, 9.0)
+# +# 4.4 (3.5, 5.3) +# +# 31.1 (29.2, 33.0) +# |
+#
|
+# Neural BF
+# +# Neural A* +# |
+#
+# 75.5 (73.8, 77.1)
+# +# 87.7 (86.6, 88.9) +# |
+#
+# 45.9 (44.6, 47.2)
+# +# 40.1 (38.9, 41.3) +# |
+#
+# 52.0 (50.7, 53.4)
+# +# 52.0 (50.7, 53.3) +# |
+#
Selected Path Planning Results
+#dummy text
+#
+# Path Planning Results on SSD Dataset
+#dummy text
+#