In the rapidly evolving field of robot technology, the demand for precise and efficient motion control in complex tasks, such as bimanual cooperative insertion, has become increasingly critical. Traditional robot control methods often rely on manually designed rules and conventional algorithms, which struggle to adapt to the精细化 and dynamic nature of fine manipulation. These limitations highlight the need for advanced learning-based approaches that can enhance a robot’s environmental perception and decision-making capabilities. Imitation learning, particularly behavioral cloning (BC), has emerged as a promising solution by enabling robots to learn from expert demonstrations without explicit programming. However, standard BC methods suffer from compound errors and poor generalization, where small inaccuracies in predicted actions accumulate over time, leading to significant deviations from desired trajectories. This paper addresses these challenges by proposing an enhanced behavioral cloning algorithm that integrates multi-scale feature pyramids and attention mechanisms to improve the precision, smoothness, and adaptability of robot motion control strategies. By leveraging advancements in robot technology, our approach aims to bridge the gap between simple training procedures and high-performance execution in fine manipulation tasks.
The core of our method lies in redesigning key components of the BC framework to overcome its inherent limitations. We introduce a backbone network that combines residual networks (ResNet) with feature pyramids to extract and fuse multi-scale image features, thereby enhancing the robot’s ability to perceive environmental details and provide accurate visual feedback. Additionally, we incorporate action chunking and temporal ensembling to mitigate compound errors by predicting sequences of actions rather than single steps, resulting in smoother and more reliable trajectories. Furthermore, we reformulate the control policy as a conditional variational autoencoder (CVAE) augmented with attention mechanisms, which learns the distribution of expert demonstrations and captures correlations between image features and actions. This allows the policy to generalize better to unseen scenarios and exhibit creative problem-solving abilities. Through extensive simulations in bimanual tasks like object transfer and peg-in-hole insertion, we demonstrate that our algorithm outperforms several baseline methods in terms of success rates and trajectory quality. The contributions of this work not only advance robot technology but also pave the way for more autonomous and adaptable robotic systems in real-world applications.
To provide a formal foundation, we frame the problem within the Markov Decision Process (MDP) framework, defined by the tuple $$M = (\mathcal{S}, \mathcal{A}, P, R, \gamma, \rho)$$, where \(\mathcal{S}\) represents the state space, \(\mathcal{A}\) the action space, \(P: \mathcal{S} \times \mathcal{A} \rightarrow \Delta(\mathcal{S})\) the transition probabilities, \(R: \mathcal{S} \times \mathcal{A} \rightarrow [0,1]\) the reward function, \(\gamma \in (0,1)\) the discount factor, and \(\rho\) the initial state distribution. The expert policy \(\pi_E\) generates a dataset \(\mathcal{D} = \{(s_i, a_i)\}_{i=1}^m\) of state-action pairs. The goal is to find a policy \(\pi: \mathcal{S} \rightarrow \Delta(\mathcal{A})\) that maximizes the cumulative reward, expressed as:
$$V^\pi = \mathbb{E} \left[ \sum_{t=0}^{\infty} \gamma^t R(s_t, a_t) \mid s_0 \sim \rho, a_t \sim \pi(\cdot \mid s_t) \right]$$
and minimizes the difference from the expert’s value function: \(\min_\pi \left[ V^{\pi_E} – V^\pi \right]\). Behavioral cloning approaches this by treating the problem as supervised learning, where the policy \(\pi_\theta\) is trained to map states to actions using the dataset \(\mathcal{D}\). However, this method is prone to compound errors, as small prediction errors accumulate over time, leading to poor performance in long-horizon tasks. Specifically, if the policy incurs a loss \(\epsilon\) at each step, the value difference scales as \(\frac{\epsilon}{(1-\gamma)^2}\), highlighting the sensitivity to inaccuracies. Moreover, BC lacks generalization to novel environments due to its reliance on static training data. Our improved algorithm addresses these issues by incorporating multi-scale feature extraction, action sequencing, and generative modeling, thereby enhancing the robustness and flexibility of robot technology in dynamic settings.
The architecture of our improved behavioral cloning algorithm consists of three main modules: the feature pyramid-based backbone network (F-backbone), the action chunking and temporal ensembling module, and the attention-based CVAE decoder (A-CVAE). The F-backbone network processes RGB images from the robot’s environment using a ResNet-18 model combined with a feature pyramid network (FPN). The ResNet component extracts hierarchical features through residual blocks, formulated as \(y = H(x) + x\), where \(H(x)\) represents convolutional layers and \(x\) is the input. The FPN then generates multi-scale feature maps by upsampling and merging features from different layers. For instance, given an input image, ResNet produces feature maps \(C_2\) to \(C_5\), which are processed by the FPN to output \(P_2\) to \(P_5\) with consistent channel dimensions. The FPN operations include upsampling \(U(\cdot)\) and convolution \(F(\cdot)\), combined as \(y = F(x) + U(y’)\), where \(y’\) is the feature from a higher layer. This multi-scale approach allows the robot to detect objects of varying sizes, crucial for fine manipulation tasks in advanced robot technology.
To reduce compound errors, we implement action chunking with a fixed chunk size \(k\). Instead of predicting a single action \(a_t\) at each time step, the policy \(\pi_\theta\) predicts a sequence of actions \(a_{t:t+k-1}\) based on the current state \(s_t\). This reduces the effective horizon of the task by a factor of \(k\), making the policy more robust to temporal variations. During execution, we use temporal ensembling to average overlapping action predictions. Specifically, at time \(t\), the final action is computed as a weighted average:
$$a_t = \frac{\sum_i w_i A[t-i]}{\sum_i w_i}$$
where \(w_i = \exp(-m \cdot i)\) are exponential weights that prioritize recent predictions, and \(A\) is a buffer storing action sequences. This ensures smooth and accurate trajectories, minimizing jerky motions that could lead to task failure in delicate robot technology applications.
The policy is trained as a conditional variational autoencoder (CVAE) to learn the distribution of expert demonstrations and improve generalization. The CVAE encoder \(q_\phi(z \mid a_{t:t+k-1}, o_t)\) infers a latent variable \(z\) representing the action type, where \(o_t\) denotes the observation (e.g., image and joint positions). The encoder uses a Transformer architecture that combines a [CLS] token, embedded joint positions, and embedded action sequences into a single input sequence. The joint positions are projected to 512 dimensions using a linear layer, and the action sequence of size \(k \times 14\) is projected to \(k \times 512\). The encoder outputs the parameters of a Gaussian distribution for \(z\), and we use the reparameterization trick to enable gradient-based optimization. The CVAE decoder, or policy \(\pi_\theta(a_{t:t+k-1} \mid o_t, z)\), generates action sequences based on the latent variable and current observation. It consists of a Transformer encoder and decoder with attention mechanisms. The Transformer encoder processes features from the F-backbone, joint positions, and \(z\), forming an input sequence of size \([(n \times 300) + 2] \times 512\) for \(n\) camera views. The Transformer decoder then uses cross-attention layers to integrate these features and predict the action sequence. The decoder includes multiple attention layers: self-attention for capturing temporal dependencies in the action sequence, and cross-attention to focus on relevant image features. The output is passed through a multi-layer perceptron (MLP) to produce the target joint positions for the next \(k\) steps. This attention-based design allows the model to prioritize critical information in the input, enhancing the precision of robot technology in complex tasks.
We evaluate our algorithm in simulated environments using MuJoCo, focusing on two bimanual fine manipulation tasks: object transfer and peg-in-hole insertion. In the transfer task, a robot must pick up a red cube with one arm and place it into the gripper of the other arm without touching the table. The reward function is defined based on contact states: reward 1 for grasping with the right arm, reward 2 for lifting the cube, reward 3 for initiating transfer, and reward 4 for successful transfer without table contact. In the insertion task, the robot must pick up a blue socket and a red peg with each arm and insert the peg into the socket, with a reward of 4 given for successful insertion while avoiding table contact. We collect 50 expert demonstrations for each task and train the policy with a chunk size \(k=100\). The hyperparameters include a learning rate of 1e-5, batch size of 8, and 6 encoder layers and 9 decoder layers in the Transformer. We compare our method (FA-BC) against five baseline algorithms: BC-ConvMLP, BeT, RT-1, VINN, and ACT. The results, summarized in Table 1, show the success rates for sub-tasks and final task completion. Our algorithm achieves the highest success rates, with 99% in transfer and 93% in insertion, outperforming other methods by significant margins, particularly in tasks requiring precise contact and alignment. This demonstrates the effectiveness of our approach in advancing robot technology for fine manipulation.
| Algorithm | Transfer Task | Insertion Task | Final Success (Transfer) | Final Success (Insertion) |
|---|---|---|---|---|
| BC-ConvMLP | 34 | 32 | 1 | 1 |
| BeT | 60 | 55 | 27 | 3 |
| RT-1 | 44 | 26 | 2 | 1 |
| VINN | 13 | 11 | 3 | 1 |
| ACT | 97 | 96 | 86 | 32 |
| FA-BC (Ours) | 99 | 100 | 93 | 52 |
To further analyze the contributions of each module, we conduct ablation studies by sequentially removing the F-backbone and A-CVAE components. As shown in Table 2, using only the ResNet backbone without FPN reduces the final success rates by 4% in transfer and 12% in insertion, highlighting the importance of multi-scale feature extraction. Removing the attention mechanisms in the CVAE decoder (i.e., using a basic CVAE) decreases performance by 2% in transfer and 4% in insertion, emphasizing the role of attention in capturing relevant features. When both modules are ablated, the success rates drop by 7% and 20% respectively, confirming that the combined design is crucial for high performance in robot technology applications. These results underscore the synergy between feature pyramids and attention mechanisms in enhancing the robot’s perceptual and decision-making capabilities.
| Configuration | Transfer Task | Insertion Task |
|---|---|---|
| FA-BC (Full) | 93 | 52 |
| Without F-backbone | 89 | 40 |
| Without A-CVAE | 91 | 48 |
| Without Both | 86 | 32 |
The motion trajectories generated by our algorithm exhibit smooth and stable joint angle changes, as illustrated in the robot’s movement processes. For example, in the transfer task, the left and right arms’ joint states and commands align closely, indicating precise control execution. Similarly, in the insertion task, the robot performs seamless motions without jitters or jumps, ensuring successful peg-in-hole alignment. These outcomes validate the effectiveness of action chunking and temporal ensembling in producing reliable trajectories for robot technology. The integration of attention mechanisms also allows the policy to adapt to variations in object positions and environmental conditions, demonstrating improved generalization over traditional BC methods.
In conclusion, this paper presents an improved behavioral cloning algorithm that addresses key limitations in robot motion control for fine manipulation tasks. By incorporating multi-scale feature pyramids, action chunking, and attention-based CVAE decoding, our approach enhances the robot’s perceptual accuracy, trajectory smoothness, and adaptability to novel scenarios. The simulation results confirm superior performance compared to existing methods, with significant improvements in success rates and motion quality. These advancements contribute to the broader field of robot technology by enabling more efficient and reliable autonomous systems. Future work will focus on reducing the computational complexity of the attention mechanisms, extending the approach to include obstacle avoidance, and validating the algorithm on physical robots to ensure real-world applicability. Through continued innovation, we aim to further push the boundaries of what is possible in robot technology, making robots more capable and versatile in complex environments.