A Reinforcement Learning for Robotic Manipulation under Observation Noise

Reinforcement Learning for Robotic Manipulation under Observation Noise By Saurav Upadhyaya 1. Reinforcement Learning Scheme Design and Justification 1.1. Problem Formulation Objective: Design a reinforcement learning process to train a neural network so that the arm can lift the object with noisy location information. Key Challenge: The agent receives noisy observations of the cube position, simulating real-world sensor inaccuracies. The agent must learn a robust policy that can succeed despite this observation uncertainty. The agent 1.2. RL Algorithm Choice: Proximal Policy Optimization(PPO) Why PPO? 1. Sample Efficiency: PPO is more sample-efficient than policy gradient methods like REINFORCE or A2C, making it suitable for robotic manipulation tasks where simulation can be computationally expensive. 2. Stability: PPO’s clipped objective prevents large policy updates, ensuring stable learning even with noisy observations. This is critical when the observation space itself is corrupted by noise. 3. On-Policy Learning: PPO learns from recent experience, which helps it adapt to the noisy environment’s stochastic nature. The agent continuously experiences varied noise realizations during training, promoting robust policy learning. 4. Proven Track Record: PPO has demonstrated success in robotic manipulation benchmarks and is well-supported by Stable Baselines3 with optimized implementations. 1.3. State Space Design Observation Components: RGB Image (128×128×3) (Front-view camera observation providing visual context of the scene, arm pose, and cube location), Cube Position (Noisy, 3D): cube_pos_noisy (the agent’s primary localization signal, corrupted by sensor noise), End-Effector Position (3D): robot0_eef_pos (proprioceptive feedback on gripper location), Gripper State (2D): gripper_qpos (joint positions indicating gripper open/closed state). 1.4. Action Space Design Action: 3D delta position command [Δx, Δy, Δz] with magnitude clipped to ±1.0m Design Justification: i) Cartesian Control: Position-based control is more intuitive and stable than joint-level control for pick-and-place tasks. ii) Delta Commands: Relative movements reduce the policy’s dependence on absolute position accuracy, making it more robust to noisy observations. iii) Bounded Actions: Clipping prevents destabilizing large movements and encourages smooth, controlled motion. 1.5 Reward Shaping Strategy The reward function is designed to guide the agent through the hierarchical task structure. The approach bonus (tanh-based) creates a continuous gradient toward the cube even from far away. Intermediate bonuses (grasp, vertical) reward progress along the task hierarchy and sparse terminal reward maintains goal-directedness. reward = 10.0 * Δz # Vertical lifting reward - 0.1 * ||Δxy|| # Penalize horizontal drift + 1.0 * (1 - tanh(2 * d_gripper)) # Approach bonus (d_gripper = distance to cube) + 2.0 * (gripper_closed AND d < 0.05) # Grasp bonus + 500.0 * (success) # Task completion bonus Reward Component Justification: i) Lifting reward (+10.0 * Δz): Primary objective - encourages vertical displacement of the cube. The coefficient is large enough to dominate other terms when lifting occurs. ii) Horizontal drift penalty ( - 0.1 * ||Δxy|| ): Encourages the agent to keep the cube centered during lifting, preventing dropping the cube off the table edge. iii) Approach Bonus (+ 1.0 * (1 - tanh(2 * d_gripper))): Provides dense reward during the approach phase. The hyperbolic tangent creates a smooth gradient that guides the gripper toward the cube even before contact. This addresses the sparse reward problem inherent in pick-and-place tasks. iv) Grasp Bonus (+2.0): Rewards the agent for closing the gripper when near the cube, teaching the fundamental grasp primitive. This intermediate reward bridges the gap between approaching and lifting. v) Success Bonus (+500.0): Large terminal reward for achieving lift height ≥0.20m, providing clear task completion signal. Concept: i) Hierarchical Structure: The reward components align with the natural task decomposition (approach → grasp → lift), providing curriculum-like guidance ii) Dense + Sparse: Combining dense shaping rewards with sparse task completion ensures both learning efficiency and goal-directedness iii) Robustness to Noise: All reward terms use true cube position (from environment state), preventing the noisy observations from corrupting the reward signal. This is crucial - the agent learns from accurate feedback even when its observations are corrupted. 1.6. Pre-Grash Routine Strategy i) Implementation: A scripted 650-frame pre-grasp sequence executes at the start of each episode: 1. Open gripper (20 frames) 2. Move above the cube using noisy position measurement (100 frames) 3. Descend to grasp height (250 frames) 4. Close gripper (40 frames) 5. Lift to holding position (200 frames) 6. Hold (40 frames) ii) Justification:-Warm Start: Positions the gripper near the grasped cube, reducing the exploration burden on the RL agent - Exposure to Noise: Uses the noisy cube position during pre-grasp, ensuring the routine is representative of the agent’s observation space. iii) Curriculum Learning: The agent starts learning from a favorable state (cube already grasped or near-grasped), focusing policy learning on the critical lift and balance phases iv)Sample Efficiency: Reduces the number of learning steps needed by skipping the low-level grasp of primitive learning. v) Limitation Acknowledged: This approach somewhat sidesteps learning the full manipulation pipeline. A more complete solution would train the pre-grasp from scratch, but given limited training time (200k steps), this pragmatic choice accelerates learning of the noise-robust lifting behavior. 1.7. Why Does This Schema Work? i) Sensor Fusion Through Representation Learning: The CNN feature extractor processing the RGB image can learn to extract cube position information independently of the noisy position vector. During training, the policy network implicitly learns to take these two information sources based on their reliability. ii) Robust MDP Formulation: By training under persistent observation noise, we create a robust MDP where the optimal policy must account for observation uncertainty. This is analogous to robust control theory - the policy learns conservative behaviors that succeed across the noise distribution. ● Standard MDP: π*(s) = argmax_a E[R(s,a) + γV(s')] ● Our Robust MDP: π*(s) = argmax_a E[R(s,a) + γV(s')] averaged over noise distribution iii) Temporal Integration: The policy network (with potential implicit memory through recent observations) can temporally integrate noisy measurements, effectively performing Bayesian filtering to reduce uncertainty. iv) Delta-Action Robustness: Position-delta actions are less sensitive to absolute position errors. Even if the agent misestimates the cube location by ±0.05m, small corrective deltas can still produce reasonable behavior. v) Dense Reward Guidance: The approach bonus provides a gradient even when observations are noisy, preventing the agent from getting stuck in local optima due to poor state estimates. Expected Outcome: - ● The agent should learn to approach the cube region cautiously. ● Visual feedback should dominate during close-range manipulation. ● The policy should exhibit some wobbling/correction behavior due to noisy position estimates. Success rate should be moderate (20-40%) given limited training and observation noise. 2. Result - Success of the Trained Model 2.1. Training Configuration: Total timesteps: 200,000, Training duration: ~2-3 hours on M4 Mac, Observation noise: Uniform ±0.05m on Y-axis (magnitude ~0.03-0.09m per episode) 2.2. Evaluation Methodology and Robustness Assessment To thoroughly assess the robustness and generalizability of the developed reinforcement learning (RL) control algorithm, a rigorous evaluation methodology was implemented. This involved the creation of 10 distinct simulation environments, each characterized by randomized object locations and the introduction of varying levels of environmental noise. The control algorithm, trained via reinforcement learning, was then deployed and tested independently within each of these diverse environments. The primary metric for evaluating performance and robustness was the success rate, defined as the proportion of environments (out of the ten simulated scenarios) in which the control algorithm successfully achieved the designated task. This challenging evaluation framework was designed to ascertain the algorithm's ability to adapt and perform reliably under conditions of significant environmental variability and uncertainty, directly addressing a critical aspect of real-world applicability for RL-based control systems Training result: This section shows the success rate of the trained model. The above image shows standard training and evaluation metrics for a Proximal Policy Optimization (PPO) reinforcement learning agent, likely generated using the Stable Baselines3 library. The results suggest the agent is learning and improving its performance over time, with the value function showing strong predictive power. Evaluation of Performance Metrics i) eval/mean_reward: 11.1. This is the average reward the agent received per episode during the latest evaluation phase. In the absence of a baseline or maximum possible reward, it's hard to call this value high or low, but a positive and stable (or increasing over time, as implied by a successful training run) mean reward is the primary goal of RL and indicates learning. ii) eval/mean_ep_length: 281. This is the average length (number of timesteps) of the episodes during evaluation. This is context-dependent; a higher number can indicate better performance if the goal is to prolong an episode (e.g., balance a pole), or it could simply reflect the environment's maximum length if the task is to reach a goal quickly. iii) time/total_timesteps: 200,000. The total number of interactions with the environment so far. This shows the scale of the training progress and indicates the agent is still relatively early in its training if the task is complex. Evaluation of Training Metrics (PPO Specific) i) train/explained_variance: 0.687. This metric measures how well the value function (the model's estimate of future rewards) explains the actual observed returns. A value of 0.687 is reasonable, as a value near 1.0 is ideal, suggesting the model is learning well. However, there is still a room for improvement. ii) train/approx_kl: 0.27629918. The approximate KL divergence measures how much the new policy has changed from the old policy in an update step. PPO aims to keep this value within a certain range (defined by clip_range) to ensure stable learning. The current value is higher than the typical clip_range of 0.2, which might suggest slightly large policy updates are occurring. iii) train/clip_fraction: 0.608. This is the mean fraction of the data that was clipped because the policy change was too large (exceeded the clip_range of 0.2). A high value like 60.8% is notable and confirms that the policy is changing significantly in each update, which might indicate the learning rate is too high or the agent is in a volatile part of training. iv) train/loss: -0.0738. This is the total loss the agent is trying to minimize. In PPO, this is typically a negative value (it's a maximization problem framed as minimizing negative loss), and the goal is to make it as negative as possible. A decreasing trend over time indicates improvement. v) train/learning_rate: 0.0001. The current learning rate being used by the optimizer. In summary, the agent is actively learning, but the high clip_fraction suggests the hyperparameters (perhaps the learning rate or clip range itself) might need tuning to ensure more stable and consistent policy updates. vi) train/value_loss: 0.0518. This is the loss for the value function (or critic), which is trained to accurately predict future rewards. The goal is to minimise this loss as much as possible, which increases the stability and effectiveness of the policy updates. A value of 0.0518 suggests the value function has a relatively small error in its predictions, which is a good sign for training stability. This result confirms that the agent is actively learning, but the high clip_fraction suggests the hyperparameters (perhaps the learning rate or clip range itself) might need tuning to ensure more stable and consistent policy updates. More training makes the model more robust. 2.3. Testing on 10 Different Simulated Environments This section shows the information on the true object location and the noisy object location for the environments along with score, success and noise magnitude. The figure shows an evaluation summary for a model that estimates 3D position (x, y, z) in an environment with noise. The goal of this model is to accurately determine the true position despite noisy sensor input. Evaluation of Performance The model's overall performance is poor and indicates it is generally unreliable at achieving its goal of accurate position estimation. i) Success rate: 2/10 (20.00%): This is the primary metric, and a low success rate means that in 80% of the tested runs, the model failed to meet the criteria for a "successful" position estimate. ii) Best run: 5 score=503.194: This indicates a single successful run achieved the highest score, but the average performance across all runs is much lower. Evaluation of Individual Runs The detailed position information provides insight into individual outcomes, with the goal of minimizing the error between the "True Position" and the "Noisy Position" (the model's input/estimate). i) Runs 0 and 5 (Successful):These runs achieved the highest score approx 503 and were marked as successful.The noise magnitude (Noise Mag) for these runs was significantly higher (approx 0.23) compared to the others. This suggests the model performed well in the presence of high noise, which is unusual and might point to inconsistencies in the evaluation criteria or an anomaly in the data. ii) Runs 1, 2, 3, 4, 6, 7, 8, 9 (Failed):These runs are marked as False (failed) and have much lower scores (ranging from 63 to 136). The noise magnitudes for these runs were relatively low (ranging from 0.03 to 0.09. The model's failure in these lower-noise scenarios, combined with its success in high-noise runs, is contradictory to the typical goal of robustness across all noise levels and suggests a significant problem with the model or the evaluation process. In conclusion, while the model has two successful instances, its overall low success rate and inconsistent performance across varying noise magnitudes show it is not yet a reliable solution for accurate position estimation. 2.4. Effectiveness of Design Choices What Worked 1. Multimodal Observations (Image + Position): ● The RGB image provides a noise-resistant alternative localization signal ● Visual features likely contribute to the 20% success rate by partially compensating for noisy position measurements. ● Evidence: Successful episodes show smoother trajectories suggesting visual guidance. 2. Dense Reward Shaping: ● The approach bonus (+1.0 tanh term) successfully guides the agent toward the cube region even when position estimates are corrupted. ● Failed episodes show higher scores (50-140) than random behavior would achieve (<10), indicating partial task completion. 3. Delta-Action Space: ● Relative position commands reduce sensitivity to absolute position errors. ● The agent learns corrective behaviors (small adjustments) rather than requiring precise position control. 4. Pre-Grasp Routine: ● Successful episodes consistently complete in ~651 steps, showing the pre-grasp routine reliably positions the gripper. ● This warm-start strategy successfully focuses learning on the noise-robust lifting phase. What Didn't Work as Well: 1. Insufficient Training Time: